Can Interventions Rescue Glennan’sMechanistic Account of Causality?∗

Lorenzo Casini†

April 7, 2015

Abstract

Glennan (2011) appeals to interventions to solve the ontological and ex-planatory regresses that threaten his mechanistic account of causality (Glen-nan, 1996, 2002). I argue that Glennan’s manoeuvre fails. The appeal tointerventions is unsuitable to address the ontological regress, and it blocksthe explanatory regress only at the cost of making the account inapplicableto non-modular mechanisms. I offer a solution to the explanatory regressthat makes use of dynamic Bayesian networks. My argument is illustratedby a case study from systems biology, namely the mechanism for the irre-versibility of apoptosis. I conclude by pointing out the implications of myargument for Glennan’s mechanistic account of causality and, more gener-ally, for accounts of mechanistic explanation based on interventions.

1 IntroductionGlennan (1996, 2002)’s mechanistic account of causality is based on the intuitionthat mechanisms are the truth-makers of causal claims and provide the causalexplanation of phenomena. A causal explanation is provided by opening the blackbox between cause and effect, that is, by pointing to the thread of intermediateevents that link cause and effect; a truth-maker is found by identifying the system’s

∗Penultimate draft. The final version is forthcoming in the British Journal for the Philosophyof Science, doi:10.1093/bjps/axv014. Advance access version available here.

†Address: Department of Philosophy, University of Geneva, 5, Rue de Candolle, CH-1211Geneve 4, Switzerland. Email: lorenzo.casini@unige.ch

constituent entities, activities and organisation which together make the causalclaim true by producing such a thread of intermediate events. The account ismeant to be applicable to the higher-level systems studied by special sciencessuch as biology, psychology, sociology, etc.

Against Glennan, Psillos (2004) and Craver (2007) have objected that the ac-count is vulnerable to, respectively, an ontological and an explanatory regress, dueto the asymmetric relation between mechanisms and counterfactuals. Recently,Glennan (2011) has tried to block both regresses. He has appealed to interven-tions and causal graph theory (Pearl, 2000; Woodward, 2003) to—in response toPsillos—argue that mechanisms and counterfactuals are on a par and—in responseto Craver—give a robust account of explanatory relevance. Interestingly, in doingso, Glennan has come very close to Craver’s own understanding of explanation.

In this paper, I show that Glennan’s manoeuvre fails. The appeal to inter-ventions is simply unsuitable to address the ontological regress. It can, I argue,address the explanatory regress, although only in restricted circumstances, namelymodular mechanisms. This leaves Glennan’s mechanistic account of causality atbest incomplete. More generally, it threatens to undermine attempts to understandmechanistic explanation in terms of interventions (Craver, 2007; Hedstrøm andYlikoski, 2010; Leuridan, 2010; Menzies, 2012). My discussion of the explana-tory regress is illustrated by a case study from systems biology, namely the mech-anism for the irreversibility of apoptosis (Legewie et al., 2006). In the case of thismechanism, I propose to use dynamic Bayesian networks (DBNs) to block the ex-planatory regress. However, I make no claim that DBNs may be used to model allmechanisms, nor that DBNs deliver a general solution to the explanatory regress.

I proceed as follows. In §2 I introduce Glennan’s account. In §3 and §4 Ipresent, respectively, Psillos’ and Craver’s objections and Glennan’s replies. In §5and §6 I argue that Glennan’s replies fail. In §7 I illustrate my proposal. Finally,in §8 I discuss the prospects of Glennan’s mechanistic account of causality.

2 Glennan’s account of causalityWhereas other mechanists (Machamer et al., 2000; Bechtel and Abrahamsen,2005) appeal to mechanisms to give an account of scientific explanation, Glen-nan uses mechanisms to analyse causality. For Glennan, a relation between twoevents is causal if and only if it is underwritten by some mechanism which con-nects them (1996, p. 64). In turn, a mechanism is defined as follows:

A mechanism for a behavior is a complex system that produces that

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behavior by the interaction of a number of parts, where the interac-tions between parts can be characterized by direct, invariant, change-relating generalizations (Glennan, 2002, p. S344).

That is, the mechanisms that enter in the analysis of causality are stable (e.g.,watches, cells, organisms, social groups), made of parts which are themselves sta-ble, and whose interactions produce a robust behaviour (Glennan, 2002, p. S345).The requirement that the interactions be ‘direct’ is meant to avoid interveningcausal factors along the pathway from cause to effect. Notice that interactions—and thus, ultimately, causation—are not directly analysed in terms of counterfactual-supporting generalisations.1 Rather, such generalisations are regarded only as away of describing the relation between facts about interventions and facts aboutthe outcomes of these interventions.

Glennan’s criterion for characterising the interactions responsible for the causalrelation draws on Woodward’s notion of invariance under intervention. However,the role of this notion in the two accounts is subtly different, and it is importantto clarify this difference to set the stage for subsequent discussion. In both cases,interventions help identify generalisations which are ‘change-relating’, i.e. func-tionally relating changes in two variables, and ‘invariant’, i.e. stable across a rangeof values of the other variables. However, Woodward uses interventions to givetruth conditions of causal claims (cf. 2003, p. 95).2 Roughly, a variable X causesanother variable Y if and only if an appropriate intervention on X would changeY . Instead, Glennan uses interventions to give the truth conditions of the coun-terfactuals describing the relation among the properties of interacting parts uponinterventions on them. More precisely, one such counterfactual is true if a possi-ble intervention on the value of a variable (which corresponds to the property ofone part) would bring about a change in the value of another variable (which cor-responds to the property of another part) (Glennan (2002, pp. S344-5), Glennan(2011, pp. 803-4)). Clearly, these counterfactuals have a causal content. However,Glennan does not take their truth conditions to provide an analysis of causality it-self. This is because, unlike Woodward, Glennan doesn’t aim at an analysis ofcausality in terms of interventions. More generally, whereas Woodward focuseson the meaning of causal claims, Glennan focuses on their metaphysical under-

1In this regard, this definition differs from the one previously offered in (Glennan, 1996, p. 54),where mechanisms are analysed in terms of laws.

2These truth conditions must be non-circular, that is, not assume the causal character of thetarget relation, although they can assume that other relations—including the intervention—arecausal (cf. Woodward, 2003, pp. 20-2, 104-5).

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pinning. For Glennan, the glue between cause and effect is a mechanism, andnot facts about interventions that merely exploit the existence of the mechanism.Mechanisms provide truth-makers. Interventions only provide test conditions. InGlennan’s own words, ‘The manipulability account emphasizes procedures fordiscovery, prediction and control. The mechanical account provides [...] a meta-physical underpinning of the manipulability approach’ (2011, p. 802) (cf. Glen-nan, 2010, p. 378).3

Glennan’s definition of mechanisms as ‘complex’ systems signals the require-ment that the systems in question be decomposable into parts and interactions.4

The special sciences often aim to explain phenomena in terms of the mechanismswhich underlie them, and many mechanisms are so decomposable. The phenom-ena of interest to the special sciences are often causal. Their explanation doesnot appeal to fundamental physical laws. So, Glennan’s proposal is interesting,because it purports to analyse causality in the higher-level systems studied by thespecial sciences in its own right, that is, in a way that does not depend on, orreduce to, an account of causality in fundamental physics.

3 Objections to Glennan’s accountGlennan’s account has been subjected to the following two criticisms. First, mech-anisms have no ontological autonomy, so do not, by themselves, ground the truthof causal claims (Psillos, 2004). Second, mechanisms—so construed—have noexplanatory autonomy either, and actually deprive higher-level causal claims oftheir explanatory power (Craver, 2007). I’ll examine these criticisms in turn.

3Does Glennan’s use of interventions really differ from Woodward’s? Providing truth condi-tions for counterfactuals that describe what happens under interventions on properties of causallyinteracting parts does sound similar to providing an analysis of the causal relation between suchproperties. Surely, if Glennan were to give such an analysis, his position would reduce to Wood-ward’s and his entire project would be doomed. I need not defend Glennan here but only state hiscase. It is not incoherent—so Glennan might reply—to hold that facts about interventions revealthe causally relevant features of the mechanism that makes some target causal claim true, and yetsuch facts do not constitute such features. So, one can use interventions to describe the mech-anism that underpins the causal relation, without at the same time analysing causality in termsof interventions. (The correctness of this reading is supported by Glennan’s own endorsement ofPearl’s semantics for counterfactuals, according to which counterfactuals have a primitive causalcontent—more on this in fn. 11.) I am grateful to an anonymous referee for demanding moreclarity on this.

4The applicability of the account to cases of systems that are complex in a somewhat strongersense—namely, non-modular systems—will be considered in §6.

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Grounding first. Psillos observes that Glennan’s analysis of mechanism givesrise to an ontological regress, which would show that between mechanisms andcounterfactuals there is an ontological asymmetry, such that counterfactuals aremore basic than mechanisms. Whether X causes Y depends on the existence of amechanism between X and Y . The mechanism is constituted by a number of parts,their interactions and organisation. The parts’ properties are related by counter-factual relations, which are described by invariant, change-relating generalisa-tions. Whether the counterfactuals are true is—epistemically—revealed by in-terventions but is—ontologically—explained by further, lower-level mechanisms.Each mechanism can be further decomposed in the above manner. Ultimately,however, one runs out of mechanisms. For systems at the bottom of the mecha-nistic hierarchy, the truth of the counterfactual relations between the properties oftheir parts is ‘brute’, that is, not mechanically explicable. Whence the asymme-try between mechanisms and counterfactuals—that is, between mechanisms andthe non-mechanical facts that make true bottom-level counterfactual statements.5

Psillos denies that mechanisms have ontological autonomy. So, Glennan’s mecha-nistic account is inadequate, as it fails to identify the truth-makers of higher-levelcausal claims.6 Still, Psillos grants that mechanisms may have explanatory andepistemic autonomy. We may, and often do, omit the description of fundamentalfacts and still get a satisfactory explanation.

Craver, instead, challenges the ability of Glennan’s account of even grantingmechanisms their explanatory power with respect to the high-level causal claimsthey underpin.7 Although sympathetic towards mechanistic accounts of explana-tion (Machamer et al., 2000), Craver does not think one can analyse causality interms of mechanisms (Craver, 2007, p. 86, fn. 17). Glennan’s mistake would be tobelieve that mechanisms explain in virtue of elucidating the secret, or necessary,connection that glues together cause and effect. But this reasoning generates, soto say, an “explanatory regress”, which deprives mechanisms of their explanatory

5Psillos’ favourite story is that such facts are fundamental laws, in the sense of a Mill-Ramsey-Lewis account (Psillos, 2004, p. 299). The details of Psillos’ position do not matter for presentpurposes.

6At most, mechanisms are part of the relevant truth-makers, if the organisation among thesystem’s parts is something over-and-above the collection of the parts (Psillos, 2004, p. 308).

7Craver (2007)’s criticism is posterior to Glennan (2002)’s endorsement of manipulationismfor the characterisation of the mechanism’s interactions. Craver (2007, pp. 86-7) acknowledgesthat, presently, Glennan’s position and his own do not differ much—which makes Craver subjectto the same criticism I raise against Glennan in §6. Craver’s focus on Glennan (1996)’s earlieraccount, which did not make use of interventions, depends on reasons of exposition, and involvesno real controversy.

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power.8 One may think that one’s grasp of the necessary connection increaseswhen one opens the black box and uncovers the mechanism that links cause andeffect. Upon realising that this link is itself composed of smaller chains of depen-dences, one then proceeds to further decomposing higher-level mechanisms intolower-level ones. Eventually, the search for the necessary connection terminatesat the level of brute counterfactuals, where the talk of necessary connections isunintelligible. ‘[H]ow can a necessary causal connection between X and Y bebuilt out of relations in which there is no necessary connection and for which suchtalk is unintelligible?’ (Craver, 2007, p. 91). This threatens Glennan’s accountof high-level explanation, insofar as this depends on an account of the necessaryconnection. The key to blocking the explanatory regress, for Craver, is to stoplooking for mechanisms that ground the truth of causal claims and instead focuson what makes putative high-level causes relevant to their effects. Craver (2007,p. 94) favours a manipulationist approach : X is, in conditions W, causally rele-vant to Y—and thus explains Y—because a change in X in conditions W wouldmake a difference to Y . In the context of a request for explanation, W describesthe conditions that are relevant in that explanatory context, which may includefacts about high-level mechanisms.

4 Glennan’s repliesGlennan (2011) has recently replied to these objections. On the one hand, heagrees with Craver on using interventions for causal explanation. Actually, if myreconstruction is correct, the role of interventions in Glennan’s account is evenmore substantial than envisaged by Craver. Interventions are used to provide ananalysis of “mechanism”. As I will show, this may—in certain cases—justify theexplanatory autonomy of mechanisms, and thus provide an account of causalitythat is not subject to the explanatory regress. On the other hand, Glennan de-nies that the use of interventions reduces the mechanistic account to a version ofthe manipulationist account—that is, he denies Psillos’ asymmetry thesis—on theground that the two theories are mutually illuminating, and either stand of falltogether.

Interventions are used not only to account for how the interactions amongthe parts explain the behaviour of the system—as in (Glennan, 2002)—but also

8Although Craver (2007, pp. 89-90)’s critique seems limited to Glennan (1996)’s ontologicalclaims on the nature of causation, Craver takes his critique to have downstream consequences onthe nature of mechanistic explanation (Craver, 2007, pp. 91-ff.), which is Craver’s main concern.

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for the broader task of giving an analysis of the mechanism responsible for thetruth of the causal claim (Glennan, 2011, pp. 802-5). This latter task is achievedby identifying mechanisms with those structures that satisfy certain modellingcriteria (Pearl, 2000; Woodward, 2003).9 A causal relation between variables Xi

and X j is characterised with respect to a causal model < U,V,E > (Woodward,2003, pp. 42-3, 339). V is a set of endogenous causal factors V = {X1, X2, ..., Xn},whose value is caused by variables in the structure. U is a set of exogenous factors,viz. outer influences (e.g., interventions) and/or errors, whose value is not causedby variables in the structure. E is a set of n structural equations describing therelations among the variables, where the value of the variables in V is a functionof variables in both U and V. For instance, to the causal structure represented infigure 1 corresponds the following set of equations:

X1 = U1 (1)X2 = f2(X1) + U2 (2)X3 = f3(X1) + U3 (3)

X4 = f4(X2, X3) + U4 (4)9A referee observes that, since (Glennan, 2005)’s view about modelling mechanisms is ‘evi-

dently pluralist’, one should not overestimate the dependence of the mechanistic account on in-terventionism. I do not agree. First, Glennan (2011, p. 805) himself admits that ‘[Woodward’s]view of what constitutes a mechanical model essentially coincides with Glennan (2005)’. Hisproposed list of adequacy criteria for mechanistic representations (cf. Glennan, 2005, p. 457) islargely compatible with interventionism. (One exception has to do with the requirement that therepresentation be temporally adequate, which is actually relevant to the failure of modularity I dis-cuss later on.) Second, Glennan (2011) adopts the manipulationist semantics of counterfactuals todeal with the explanatory regress. If he were pluralist, it would follow that he needs—but does nothave—additional criteria besides interventions to deal with the regress when the manipulationistsemantics doesn’t apply, as well as some argument on why the account still deserves being called“mechanistic” and not just “pluralist” (cf. §7).

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A causal graph is such that an arrow runs from a variable to another if thelatter is caused by and a function of the former. Variables are caused by othervariables either directly, through simple arrows, or indirectly, through chains ofarrows. In figure 1, the latter case is illustrated by the causal relation between X1

and X4, which operates along two paths, one via X2 and one via X3. Arrows standfor relations that are invariant under intervention. In Woodward’s terminology, Xi

causes X j if there is an ideal intervention I on the putative cause Xi that changesthe putative effect X j, and such that relative to V the intervention fulfils certainconditions (Woodward, 2003, p. 98), which ensure that any change in X j followingthe intervention is to be ascribed to Xi.10 Pearl’s semantics makes use of the do-operator instead of ideal interventions (Pearl, 2000, chap. 7), but is similar in spiritto Woodward’s semantics (cf. Woodward, 2003, p. 38).11

A mechanistic model is defined by Woodward with respect to such a represen-tation by means of the following necessary condition:

(MECH) a necessary condition for a representation to be an accept-able model of a mechanism is that the representation (i) describe anorganized or structured set of parts or components, where (ii) the be-havior of each component is described by a generalization that is in-variant under interventions, and where (iii) the generalizations gov-erning each component are also independently changeable, and where(iv) the representation allows us to see how, in virtue of (i), (ii), and(iii), the overall output of the mechanism will vary under manipula-tion of the input to each component and changes in the components

10Depending on whether one tests for total causation (along all chains between Xi and X j),contributing causation (along one chain) or direct causation (the simple arrow Xi → X j), differentsets of variables have to be controlled for—respectively: {∅}; V minus the variables on the chain;and V minus {X j} (cf. Woodward, 2003, pp. 51, 59).

11 A—notable—difference is that in Pearl’s definition of intervention (Pearl, 2000, p. 70) thenotion of causation between putative cause and putative effect appears as primitive, so his do-calculus cannot serve the purpose of a semantic analysis of a target causal relation (cf. Woodward,2003, p. 110). It is no coincidence that Glennan, who generally puts Pearl and Woodward inthe same bag, explicitly endorses Pearl’s semantics of counterfactuals but doesn’t mention Wood-ward’s. Glennan’s use of interventions is not aimed at an analysis of direct causation, but ratherat a characterisation of mechanisms. If it was possible to analyse direct causation in terms ofinterventions, as Woodward has it, then Glennan’s analysis would collapse into Woodward’s, andhis interventionist characterisation of mechanisms would be redundant. In spite of this differencebetween Pearl and Woodward, the two agree on the connection between the notion of interventionand the modularity assumption, which is key to Glennan’s argument, and thus to the present paper(see below).

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themselves (Woodward, 2002, p. S375) (quoted in Glennan, 2011,p. 804).

For instance, in the model in figure 1 all direct relations along the two paths be-tween X1 and X4 are invariant under intervention, and one may affect each oneof them without disrupting the others. Woodward calls (ii) “level invariance”, orinvariance within individual equations, and (iii) “equation invariance”, or invari-ance between equations (2003, p. 329). Satisfaction of (ii) and (iii) ensures thatthe system is modular. Modularity is defined as follows:

MODULARITY. A system of equations is modular if (i) each equa-tion is level-invariant under some range of interventions and (ii) foreach equation there is a possible intervention on the dependent vari-able that changes only that equation while the other equations inthe system remain unchanged and level-invariant (Woodward, 2003,p. 329).

Woodward’s notion of modularity is typically associated with the independentchangeability of the value of l.h.s. variables (see, e.g., Glennan, 2011, pp. 803-4). This requirement is encapsulated in the equation invariance condition. Forinstance, if the system in figure 1 is modular, one may change the value of X2 bybreaking the arrows into X2 without affecting the coefficient of the other relations,say, between X2 and X4. Equally important is the other condition, though, namelylevel invariance, which ‘concerns whether an individual equation is invariant un-der interventions on its r.h.s. variables’ (2003, p. 329). For instance, the relationbetween X2 and X4 is level-invariant if the coefficient of the relation between X2

and X4—as well as those of the other relations in the equation—is invariant un-der (some) interventions on X2 that modify X4 as described by the equation. Theimportance of level invariance may be explained by the intuition that all relationsare underpinned by (sub)mechanisms that are independently testable. Modular-ity then guarantees that there are interventions by which one can test individualrelations, or chains of them, on the assumption that all other coefficients remaininvariant under the intervention. This, in turn, guarantees that one may use causalmodels for counterfactual reasoning: ‘the independence of the various compo-nents allows one to trace out the consequences of possible changes in any of themfor the overall behavior of the system’ (Woodward, 2002, S374). For instance,one can use equations (2) and (4) to calculate the effect of a change in X1 on X4

along X2, relative to some fixed value of X3.

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The role of modularity in the mechanistic account will be further discussed in§6. For the moment, it is important to stress that the mechanism that the inter-ventions help identify may be correctly described in the vocabulary of the causalmodel, without reference to facts at the bottom of the mechanistic hierarchy, wherethe truth-makers are (arguably) located. Thus, it seems that one may accept thatmechanisms are not ontologically autonomous and yet maintain that they are ex-planatorily autonomous.12 Notice that Glennan’s argumentative strategy commitshim to the claim that mechanism may be correctly described in the aforemen-tioned sense. Otherwise, he could not use the interventionist machinery to blockthe explanatory regress.

Next, in response to Psillos, Glennan argues that the use of interventions inthe mechanistic account does not reduce the mechanistic account to Woodward’smanipulability account. Counterfactuals and mechanisms are on a par. Glennan’s“symmetry thesis”, as I will call it, reads:

The mechanical approach relies on the counterfactual approach be-cause there is no way to define interactions between parts of mecha-nisms except by appeal to counterfactual-supporting generalizations.The counterfactual approach relies on the mechanical approach be-cause the truth-conditions for counterfactuals depend upon the struc-ture of mechanisms (Glennan, 2011, p. 806).

Glennan grants to Psillos that the mechanical approach cannot eliminate coun-terfactuals, and so cannot provide a reductive analysis of causal claims. Yet, herejects Psillos’ asymmetry thesis on the ground that the truth conditions of coun-terfactuals depend—more or less explicitly—on the structure of mechanisms, andso one cannot do away with mechanisms either.

This symmetry would show up when the mechanist and the manipulationistaddress charges of prima facie circularity. Their strategies would entail that mech-anisms and counterfactuals are on a par—actually equally bad—in addressing thegrounding issue. Hence, counterfactuals aren’t more basic than mechanisms. Onthe one hand, mechanisms are identified by interventions on their parts, which inturn correspond to further, lower-level mechanisms, whose relevance is tested byother interventions, and so on. On the other hand, an intervention I figures in the

12For Woodward a description is ‘correct’ (relative to some variable set) if it can be singled outof observationally equivalent ones by means of interventions. To this end, modularity is neces-sary: only if the system is modular can one ‘correctly and fully’ represent causal relations andmechanisms (2003, p. 332) (cf. Glennan, 2011, p. 804). More on this in §6.

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truth condition of a causal relation between X and Y only if the (counterfactual)relation between I and X is itself causal, which is tested by further interventions,and so on. Glennan contends that the manipulationist ‘uses the same strategy torespond to that circularity’ (2011, p. 807):

To determine whether two variables X and Y are causally related, wemust know something about other causal relationships (e.g. betweenan intervention I and X), but not about the causal relationship betweenX and Y we are seeking to establish. True enough, but how do weknow that an intervention I causes a change in X? Presumably weknow this because there is a mechanism connecting I and X (Glennan,2011, pp. 807-8).

At each step the two accounts are mutually dependent, until one reaches the levelof counterfactuals that are not underpinned by mechanisms. So, in the end neitheranalysis can ground the truth of causal claims:

Just as the mechanist must ultimately run out of nested mechanisms,Woodward must ultimately get to an intervention that cannot be fur-ther analysed in terms of interventions on further mechanisms. Oneis left with the brute claim that if one were to intervene on X a changein Y would result. But this is not an analysis of the counterfactualdependence of X and Y; it is just a restatement of it. Thus I wouldcontend that the truth of causal claims according to the manipulabil-ity theory will depend upon an unanalysed notion of counterfactualdependence (Glennan, 2011, p. 808).

Both replies rely on interventions. However, as I will argue, neither of them issatisfactory. The reply to Psillos is not satisfactory in some fundamental sense, be-cause the mechanistic account and the manipulationist account are subject to dif-ferent kinds of regress (§5). Reference to interventions may—in certain circum-stances—address the explanatory regress. When this is so, Glennan’s reply toCraver is satisfactory—although this requires some argumentation. However, theexplanatory regress is successfully addressed only when the modularity assump-tion is satisfied (§6). Since modularity is violated in many higher-level systems,the reply to Craver is not satisfactory either. In fact, when modularity fails, mech-anisms are left with no clear interpretation. This threatens the explanatory auton-omy of many mechanisms in the special sciences, which Glennan’s account ofcausality was supposed to vindicate.

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5 Ontological symmetry?In a nutshell, Glennan’s reply to Psillos was: both the mechanistic account and theinterventionist account are trapped in a conceptual circle and subject to a regress.At all non-fundamental levels the two accounts are informative about truth con-ditions of counterfactuals only in tandem. And at the fundamental level neitherone is better than the other at explaining what makes brute counterfactuals true.Thus, Psillos’ asymmetry thesis is false: counterfactuals are not more basic thanmechanisms. The problem with this reply is that the regress generated by themanipulationist circle is different from the regress generated by the mechanisticcircle. I’ll discuss the two horns in turn.

First horn: at non-fundamental levels the two accounts are informative only intandem. Are they? Glennan insists that at each step in the manipulationist regressa sort of grounding is required, namely a mechanism must exist underlying the tar-get counterfactual. Let us assume that this is the case.13 Still, the manipulationistregress is different from the mechanistic regress. Ironically, Glennan’s own argu-ment for the explanatory autonomy of higher-level causal claims depends on thetwo regresses being distinct, and on the possibility of solving the manipulationistregress independently of the mechanistic regress. Let me explain.

The mechanist decomposes one higher-level mechanism into lower-level mech-anisms, which constitute the original mechanism, in order to ground the truth ofone and the same causal claim. The regress here is ontological: the truth-makerof the causal claim is a mechanism, the truth-maker of the counterfactuals de-scribing the relations among the mechanism’s parts are lower-level mechanisms,and so on, from higher to lower levels. And since no mechanism grounds thetruth of bottom-level counterfactuals, the account does not ground the truth of theoriginal, higher-level causal claim. The manipulationist, instead, moves from onemechanism to the next to ground the truth of different causal claims—involvingthe counterfactual relations between X and Y , I and X, and so on. It is not evenclear that this involves a move from higher to lower levels. Two successive mecha-nisms need not be at different levels, but may very well be at the same level. Whatis clear is that one moves to larger and larger mechanisms, including more andmore interventions: first I; then I∗; and so on. If I am right, the manipulationistdoes not face the same regress as the mechanist.14 Most importantly, the nature of

13Below I consider bottom-level counterfactuals, where this is not the case.14 Woodward (2008, §8) himself discusses how, it at all, his account is applicable at the stage

where the (different) regress I depict bottoms out, namely where one has included more and moreinterventions until the mechanism coincides with the whole universe, and there is nothing left that

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the regress is different.Woodward’s account generates, if at all, a conceptual regress. This regress

depends on the non-reductive character of his analysis: the truth conditions of “Xcauses Y” mention a causal relation between some I and X; the truth conditionsof “I causes X” mention a causal relation between some I∗ and I; and so on. Eachstep targets a different causal relation, and it is successful if an adequate concep-tual analysis is produced. This need not involve the search for truth-makers. Ananalysis is judged appropriate when the right invariances under intervention areidentified, not when the truth-makers are reached. Woodward is quite explicitthroughout his book that his project aims at elucidating the meaning of causallocutions in terms of interventions (Woodward, 2003, p. 38), and that this thesisabout meaning does not depend on the facts about interventions being groundedin, or backed by, more fundamental facts (p. 172). The manipulationist account‘requires only that there be facts of the matter [...] about which counterfactualclaims about the outcome of hypothetical experiments are true or false’ and ‘nometaphysical picture of the “truth makers” ’ (p. 121).

Notice that some claim that Woodward fails to escape the conceptual regress(Baumgartner, 2009). This would suggest that there may be conceptual reasonswhy Woodward is forced to look for a solution to the ontological problem. I neednot defend Woodward here, since this is not Glennan’s objection. To recall, Glen-nan’s point is that in order for an intervention I to cause a change in X, theremust be a mechanism connecting I and X; or that—using terms that Glennan usesinterchangeably—in order to know that an intervention I causes a change in X,one must know that there is a mechanism connecting I and X. This may wellbe true, but is orthogonal to Woodward’s project. Since Woodward’s analysis isconceptual, rather than epistemological or metaphysical, at no stage is Woodwardforced to assume that the mechanism that makes true the claim about the inter-vention exists or is known. Nor, so Woodward claims, is he forced to mentiona mechanism at all. All he needs to mention is facts about (ideal) interventions.This latter claim is obviously contentious. The point—which Glennan does notseem to appreciate—is that, since Glennan explicitly endorses the manipulationistsemantics of counterfactuals, his own analysis of causality depends on Wood-ward’s analysis of mechanism. If the interventionist framework were unsuitable

could be used to further intervene on it. Irrespective of whether one takes the manipulationistregress to move across the levels or at the same level, I hold the view that the nature of the regressis conceptual. The conceptual point I am about to make does not hinge on one’s preferred inter-pretation of the regress’ direction—it applies in the same way to the two bottoming-out scenarios,namely to interventions at the fundamental level and interventions on the whole universe.

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to analyse the counterfactuals, and thus the notion of mechanism, Glennan couldnot make use of it to deal with the explanatory regress in the way he does.

Relatedly, consider the second of Glennan’s horns: the two accounts are ona par when confronted with the truth of bottom-level counterfactuals. Are they?Whereas for the mechanist these counterfactuals are brute because there are nounderlying mechanisms, for the manipulationist they are brute because not un-derpinned by stronger invariances at lower levels of analysis. Admittedly, whybottom-level counterfactuals are true or false cannot be explained by stronger in-variances. But this issue, so the manipulationist might insist, is immaterial towhether bottom-level counterfactuals are true or false. Their truth conditionsare—basic—facts about interventions, such that the analysis simply reads: “Ifone were to intervene on X a change in Y would result”. Glennan asserts that ‘thisis not an analysis of the counterfactual dependence of X and Y; it is just a restate-ment of it’ (2011, p. 808). However, the manipulationist could deny exactly this.Interventions provide truth conditions all the way down, that is, they do the jobthe manipulationist wants them to do. This is orthogonal to whether they identifythe truth-makers, that is, to whether they do the job the mechanist would like themto do.

Let’s wrap up. Glennan’s symmetry thesis uses interventions to characteriseinteractions as well as entire mechanisms by giving the truth conditions of cer-tain counterfactuals. Such truth conditions may or may not pick out truth-makers.As it happens, interventions do not help Glennan in addressing Psillos’ asymme-try charge, which is about truth-making. Too bad. However, truth-making wasnever the manipulationist’s concern. If one then assumes, as Glennan does, thatthe interventionist’s semantics of the counterfactuals is adequate, which is keyto Glennan’s own argument for explanatory autonomy, it follows that Glennan’sargument for the ontological symmetry fails.15 I won’t have anything more con-structive to say on how to solve the grounding problem. Yet, perhaps not all islost. Perhaps Glennan can still use interventions to develop an analysis of “mech-anism” along the lines sketched in §4. If successful, this move would justify—with some argumentation—the explanatory autonomy of mechanisms. If one iswilling to give up the ambitious project of explicating the nature of the necessaryconnection, this result should be welcomed as valuable enough, independently ofwhether the mechanistic account can address the grounding problem.

Notice that the symmetry thesis states that there is a conceptual circularity be-tween mechanisms and interventions. Conceptual circularity is not bad in itself.

15I thank two anonymous referees for demanding more clarity on this point.

14

Consider the case of non-reductive analyses of “causality” in terms of, say, “prob-abilities” among classes of events in causally homogeneous contexts, or ideal“interventions” which change the effect variable by causing a change in the causevariable. Such analyses may be informative, provided the analysans does notmention the very same causal relation which appears in the analysandum. Anal-ogously, the non-reductive analysis of “causality” in terms of “mechanism” and“intervention” may be informative, too, provided the circular connection between“mechanism” and “intervention” is virtuous.16 A consequence of having a virtu-ous circle is that at no point one is forced to leave the circle. Woodward’s circle—which results from the conceptual regress—is virtuous in this respect: one movesfrom one intervention to the next, each intervention providing evidence for the(different) causal relation it is supposed to test (Woodward, 2008, §5). Glennan’scircle—which results from the ontological regress—seems vicious: at the bot-tom of the mechanistic hierarchy Glennan cannot characterise the truth-makers interms of the mechanism-intervention symmetry.17 However, if the task is to givean adequate analysis, one need not reach the bottom of the mechanistic hierarchy.One may stop at the level where the interplay between mechanisms and interven-tions is virtuous, that is, the level where identity conditions for the mechanismwhich explains the truth of the causal claim can be provided. To this end, one mayuse causal models. But even this move is problematic.

6 Explanatory symmetry?Interventions might help Glennan justify the explanatory autonomy of mecha-nisms. Although Glennan maintains that causality is best analysed in terms ofmechanisms, and cannot be reduced to the notion of intervention, he does inter-pret the counterfactuals describing direct interactions among the parts of mech-anisms as stating what would happen under the result of interventions in Pearl’sor Woodward’s sense. The idea is that interventions, as described in the causalmodelling literature, are suitable to identify and study such mechanisms, and thus

16I am grateful to Julian Reiss for pointing this out to me.17Glennan’s choice is to resort to other means, viz. powers. This is meant to avoid reference

to laws and justify the ‘singularist’ character of the mechanistic account (Glennan, 2011, p. 812).However, for Glennan bottom-level dispositional relations, on which the truth of the counterfac-tuals is grounded, do not constitute mechanisms. Since the proposed solution to the groundingproblem involves neither the mechanism-intervention symmetry nor a power-based analysis ofbottom-level mechanisms, it is immaterial to the present discussion, which is about stopping theontological regress by reference to the notion of mechanism.

15

to determine when and how causal relations—which result from the operation ofmechanisms—obtain or break down. One can then explain causal relations interms of such counterfactuals.

As I said, for Woodward the connection between “causality” and “mecha-nism” goes via the (necessary) condition for a causal model to represent a mech-anism, that is, via the notion of modular causal structure. It is not clear whetherWoodward is committing himself to the claim that there are no causal relationsin non-modular systems. At any rate, what is relevant to the present discussionis not to establish whether Woodward’s account of causality requires modularity.Under the assumption that Glennan’s account does not reduce to Woodward’s, thiswould be a problem for Woodward, not for Glennan. Rather, what is relevant iswhether modularity is required by the manipulationist semantics of counterfactu-als, which is used by Glennan to analyse mechanisms. Is modularity necessaryfor the manipulationist semantics to deliver correct results about what happens tosome variables when one intervenes on other variables?

It should be noted that Glennan claims neither that all mechanisms are modu-lar nor that causation can obtain only in modular systems.18 So, he wouldn’t en-dorse the view that causality requires modularity. Still, if my argument is correct,Glennan may need modularity after all for his analysis of “mechanism” in termsof “intervention” to be virtuously circular and, consequently, for the mechanism-intervention symmetry to illuminate “causality”. In order to show this, in whatfollows I will be relatively non-committal on the exact characterisation of inter-ventions.19 I will only assume that the interventions I must fulfil some minimalcondition with respect to the tested relation between X and Y , so that my remarkscan apply to all manipulationists. The condition is: I must affect Y , if at all, onlythrough X, that is, not directly or through variables along a path that does not gothrough X. This condition is, without further assumptions, insufficient for causalinference. However, it is necessary to rule out invalid causal inferences based oninterventions. When the condition isn’t fulfilled, as we shall later see, interven-tions are not as informative on the structure of the mechanism and the truth of theexplanatory counterfactuals that describe the interactions among its parts.

18Personal communication.19The manipulationist notion of intervention is very specific. Instead, Glennan does not use

any specific notion of intervention—which makes him subject to at least two objections. First: thealleged solution to the explanatory regress is conditional on choosing the right kind of intervention.Second: Glennan’s argument is problematic, irrespective of his favourite notion of intervention, aslong as interventions satisfy some plausible necessary condition. In the following, I object alongthe latter lines.

16

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Consider the following example from (Woodward, 2003, p. 330), where thecausal structure of some mechanism is correctly represented by the following setof structural equations (see figure 2):20

Y = aX (5)Z = bX + cY (6)

This system is modular because there are interventions IXY on X with respect to Ythat do not interfere with (6), and there are interventions IXZ and IYZ on X and Ywith respect to Z that do not interfere with (6). Next, consider the observationallyequivalent set of equations:

Y = aX (7)Z = dX (8)

where aX is substituted for Y in (6), and d = b + ac. This second system is non-modular because interventions IXY on X with respect to Y change d as well as Y ,by modifying a.

The key role of the modularity assumption is evident if one wants to use (8)to predict the value of Z upon an intervention IXZ that changes X from its actualvalue x@ to a counterfactual value xc. If (8) is correct, one should observe zc =

dxc = (b+ac)xc. Since by assumption (6) is correct, the value calculated by meansof (8) should be the same as the one calculated by using (6), namely zc = bxc +cY .Solving for Y , the value at which one should hold fixed Y for (8) to deliver correctresults is precisely equal to axc. That is, the value of Y systematically correlateswith Z as a result of an intervention on X—which is odd, if Y and Z are variableswhose correlation is screened off by X, as the system (8)-(8) says. The most

20For simplicity, the error terms are omitted.

17

natural explanation is that either Y causes Z or the other way round. Conversely,if one wants to use (8) to predict what happens to Y after an intervention IXY onX, one observes that the value at which Z is to be held fixed does not correlatewith changes in Y , as if Z did not cause Y . So, (6)-(6) is “more correct” than (8)-(8). Insofar as (8) is at all useful for prediction under intervention, this is becauseof facts described by the modular system (6)-(6). That is, the manipulationistsemantics presupposes modularity.

This seems good news for the mechanist. If correct, the above argumentshows that the symmetry thesis is true and that the symmetry is virtuously cir-cular. Not only does one need interventionist counterfactuals to characterise theinteractions, one also needs knowledge of mechanisms to get the truth conditionsof the counterfactuals. More precisely, one needs to assume facts about mech-anisms (viz. their modularity) which make the interventions be reliable tools ofinference. But here the problem simply arises that (1) not all mechanisms aremodular, and (2) many legitimate causal claims do not depend on the operationof modular mechanisms. I will illustrate this claim with a biological example.21

This will help me conclude that the notion of intervention fails to shed light onthe notion of mechanism in a large and important class of complex systems thatGlennan’s account was supposed to cover. Note that nothing in what follows pre-supposes a definition of complex system, only an intuitive agreement that certainsystems count as complex, and thus fall within the scope of Glennan’s account ofcausality.22 The case in question is about the causal role of the protein XIAP forapoptosis. This mechanism, investigated in (Legewie et al., 2006), serves well mypurpose because it takes place in a system, viz. the cell, which is intuitively com-plex. And since the features that make for complexity in this case (viz. cycles) arecommon to many other systems—chemical, biological, ecological, social, etc.—my claim generalises to such systems, too.

Apoptosis is the mechanism by which damaged cells disintegrate.23 A signalalerts the mechanism that the cell’s DNA is damaged. In certain circumstances,

21A case against modularity was made by Cartwright (2001). However, it was objected that shefails to produce convincing counterexamples because she misinterprets the notion of modularityin invariance accounts, which consists in the possibility ‘to perform localized interventions on[the system], that is, to introduce new causes of a component and disconnect it from other causeswithout changing mechanisms elsewhere in the system’ (Steel, 2008, p. 84) (cf. Pearl, 2008, p. 74).Irrespective of the merits of the two positions, the case I present here counts as a more seriouscounterexample to modularity on the manipulationist’s own terms.

22For an influential overview on biological complexity, see (Mitchell, 2003).23For a detailed exposition, see (Weinberg, 2007, chap. 9) and (Klipp et al., 2009, pp. 132-5).

18

Figure 3: Main regulatory interactions in the intrinsic and extrinsicpathways. Green lines indicate positive regulation; red lines indicatenegative regulation. The gray shaded area represents the mechanismfor the irreversibility of apoptosis. Reproduced from (Legewie et al.,2006, p. 1062).

19

apoptosis is selected as the best response to the damage. The mechanism results,via the interactions of many genes and proteins, in a cascade of caspases (a familyof enzymes) that break their target proteins and eventually determine the disinte-gration of the cell. Along the so called “internal pathway” (figure 3), this processculminates in the irreversible activation of caspase 3 (henceforth, Casp3) via theprior activation of caspase 9 (Casp9) by the apoptotic protease activating factor1, in short Apaf1, the inhibition of Casp9 and Casp3 by the inhibitor of apoptosisXIAP, Casp9 activation of Casp3, and Casp3 positive feedback on Casp9. Moreprecisely: Apaf1 promotes apoptosis by activating Casp9, which then activatesCasp3. Casp3 activation is normally bistable, i.e., it eventually settles into an ei-ther very low or very high state. Above a threshold, Apaf1 activates Casp3 (figure4, top right). Below the threshold, instead, active Casp9 is inhibited by XIAP andunable to trigger Casp3 (top left).

Legewie et al. (2006) ask what explains irreversibility in the presence of bista-bility, that is, why Casp3 activation is irreversible when Casp3 is activated in anall-or-nothing fashion. They find that Casp3 positive feedback on Casp9 is byitself insufficient. XIAP plays a key role: not only does XIAP typically inhibitCasp3 activation via the inhibition of Casp9, it also promotes Casp3 activation,thanks to XIAP’s capacity to bind to Casp3. Although the latter capacity directlyinhibits the activation of Casp3, it also establishes the “implicit” positive feedbackof Casp3, as follows (figure 4, top right): active Casp3 sequesters XIAP away fromApaf1-associated Casp9 (“redistribution”) (bottom right); as a result, XIAP can-not bind to, and inhibit, Casp9, which is then free to activate more Casp3. Casp3activation is irreversible because, even if Apaf1 stimulation is removed, Casp3retains XIAP (bottom left). Whereas bistability is observed also when the Casp3feedback alone is present, irreversibility requires the implicit feedback, too. Thisis inferred on the basis of an intervention that mutates XIAP so that it can bind toCasp3 and Casp9 “non-competitively”, which results in increasing the probabil-ity of XIAP binding to Casp9 (because XIAP binding to Casp3 at one time has asmaller effect on preventing further XIAP from binding to Casp9 at later times)and thus eliminating the irreversibility of Casp3 activation in the bistable regime.So, the authors conclude, XIAP causes the irreversibility of Casp3 activation inthe presence of bistability.

But can one use interventions to describe the mechanism that underpins thiscausal claim in the way presupposed by manipulationism? It seems not. Interven-tions on XIAP that affect irreversibility in the presence of bistability may make adifference to Casp3 along the implicit feedback cycle only at the cost of interferingwith XIAP’s direct inhibition of Casp3. This may be better understood by refer-

20

Figure 4: XIAP-mediated Feedback. Top left: Casp9 is inhibited byXIAP, so that Casp3 is inactive. Top right: upon stronger stimulationof Apaf1 some Casp9 escapes XIAP-mediated inhibition and activatesCasp3, which then sequesters XIAP away from Casp9 (redistribution).Bottom right: redistribution results in strong activation of both Casp9and Casp3. Bottom left: the system remains in an active state evenif the stimulus is reduced. Reproduced from (Legewie et al., 2006,p. 1066).

21

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ence to the cyclic causal graph of figure 5. Apaf1’s promoting activity and XIAP’sinhibitory activity trigger two cycles.24 First, there is the positive cycle, which in-volves the Casp3-Casp9 feedback. Second, there is the implicit cycle, wherebyCasp3 reduces the level of free XIAP, which would otherwise prevent Casp9 fromstimulating Casp3. Although XIAP causes Casp3 irreversibility via Casp9, theremay be no way to directly test for this contributing effect. Any attempt may resultin either directly manipulating the effect in an attempt to control for XIAP’s directinhibition of Casp3—in violation of level invariance—or in destroying the causalconnection between XIAP and Casp3—in violation of equation invariance. Eitherway, in a violation of modularity. I will further clarify this point in §7, in relationto my own proposal.

The problem then generalises. In systems with feedbacks, modularity can fail.In such cases, interventions may not be the right tool to study mechanisms andjustify their explanatory autonomy. Upshot: Glennan’s mechanism-interventionsymmetry thesis sheds light on causality only at the cost of making the mecha-nistic account inapplicable to the (many) non-modular systems one finds in thespecial sciences. The same objection applies to those who propose to use inter-ventions to give an account of mechanistic explanation (Craver, 2007; Hedstrømand Ylikoski, 2010; Leuridan, 2010; Menzies, 2012).

Notice that the problem may seem to affect Woodward, too. If modularity fails,his analysis of mechanism in terms of modular causal structure is at most suffi-cient, not necessary. However, the following reply is open to Woodward: it doesnot matter if one’s epistemology and one’s conceptual analysis come apart. One

24Since the reactions by which XIAP inhibits Casp3 is reversible, there is an arrow from C3 toX, too. The arrow is omitted from the scientists’ simplified diagrams in figure 3 but included intheir list of reactions (cf. Legewie et al., 2006, pp. 1062-3).

22

might deny that non-modular systems count as mechanisms, and insist that thereare mechanisms only at the (lower) level where the relevant invariances are found(cf. Woodward, 2003, p. 338). Or one might simply insist that it is the in-principlepossibility of the right kind of interventions that makes the manipulationist inter-pretation of causal models an adequate analysis of mechanisms. After all, it is thetruth conditions of causal claims that the manipulationist is after, and not their testconditions. In this regard, it doesn’t matter that the scientists’ causal inference tothe role of XIAP in the mechanism depends on a structure-altering intervention.True, their intervention on XIAP affects irreversibility of Casp3 activation by al-tering the coefficients of relations between XIAP on the one hand, and Casp3 andCasp9 on the other. But this just means that their procedure doesn’t meet the ma-nipulationist standards, so they have no good justification for the causal inference.It need not entail that the right kind of intervention is in principle impossible.25

However, the same reply does not seem open to Glennan. His account of causalityis meant to be based on a scientific understanding of mechanisms at the level atwhich mechanisms are found and described by scientists themselves. In scien-tific discourse, mechanisms do have explanatory autonomy even if they are notmodular and not studied by strictly manipulationist criteria, as the above exampleillustrates.

7 A solution to the explanatory regressRequiring that the mechanism is defined in terms of a modular causal structureof counterfactuals, whose truth conditions depend on surgical interventions, isclearly too strong. In structures with overlapping cycles, there may be no suchinterventions. Arguably, an analysis of mechanisms suitable for a mechanistic ac-count of causality should be based on weaker assumptions than modularity, whichallow one to represent mechanisms also at levels where modularity is violated, anddependences between explanatory variables that cannot be cashed out in terms ofsurgical interventions.

There is a vast literature on methods for discovering mechanisms (Bechtel andRichardson, 1993; Wimsatt, 2007; Steel, 2007). Perhaps Glennan is implicitlyaccepting such methods as relevant to the explanatory autonomy of mechanisms.

25This is not to say that I find this reply satisfying. One may refuse to attach the label ‘mecha-nism’ and ‘causal’ to, respectively, structures and relations that do not meet certain requirements.However, if this move flies in the face of scientific practice, one’s notions are at least descriptivelyinadequate.

23

After all, he does take the mechanistic account to provide ‘different sorts of re-sources for discovery and prediction’ from the manipulationist account (Glennan,2011, p. 802). But then it is not at all clear why he relies so heavily on the interven-tionist machinery and the modularity assumption. Other methods may be betterequipped to cope with non-modular systems. My proposal is that, in the mecha-nism for the irreversibility of apoptosis, the dependences between the explanatoryvariables are represented by time-indexing the variables in the cyclic graph so asto generate an acyclic graph. When a probability distribution is associated to theacyclic graph, one gets a dynamic Bayesian network (DBN).

DBNs allow one to represent probabilistic dependences in cyclic mechanisms(see Clarke et al., 2014, and references therein). A DBN is constructed in twosteps. First, a prior Bayesian network is specified, which represents the probabilitydistribution of the variables at the initial time 0. Ideally, the chosen prior networkmust be the DAG that approximates best the Markov condition—which says thatany variable is conditionally independent of its non-descendents given its parents.Then, a transition network is specified, which represents the distribution of thevariables at time 1 conditional on that at time 0 (both its direct causes and its priorstate). This last step is repeated several times if needed. It is assumed that theprocess in the DBN is Markovian: the variables at time t + 1 are probabilisticallyindependent of those at times 0, . . . , t − 1 conditional on those at time t.26 27 Itis also commonly assumed that the process is stationary, i.e., the distribution ofvariables at one time conditional on those at the previous time does not vary overtime, so that the transition network remains valid for the transition between anypair of successive time periods.

The mechanism for the irreversibility of apoptosis can be modelled by un-rolling the cycles into the DBN in figure 6. In the prior network, Apaf1 stimulatesCasp9, and XIAP inhibits Casp9 and Casp3. In the transition network, Casp3activates Casp9 and vice versa, and Casp3 contributes to free XIAP. There stillis a connection between interventions and the mechanism, so represented, but itis weaker than in modularity accounts. An intervention at some time on a vari-able that points to other variables in the cyclic graph does result in downstream

26Dependences between the same variables indexed at different times are non-causal. In theDBN in figure 6, for any two t and t + 1, the arrows A1t → A1t+1, C9t → C9t+1, Xt → Xt+1,and C3t → C3t+1 are non-causal. Since not all arrows are causally interpretable, only the Markovcondition, and not the causal Markov condition, is satisfied.

27The use of a DBN to model this mechanism is orthogonal to Hausman and Woodward (1999,2004)’s claim that modularity entails the causal Markov condition. Even granting this, the Markovcondition may still be satisfied when modularity fails.

24

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changes in one or more variables, along distinct causal processes, at later times.However, there is no guarantee that for all explanatory counterfactuals describ-ing relations among two variables at different times one can actually interveneon the antecedent that affects the consequent along one path whilst controllingfor—without disrupting—the other path. Consider the causal dependence alongX0 → C90 → C31. First, interventions aimed at testing this chain are only allowedto change the causal structure by breaking arrows into X0. For instance, one can-not modify XIAP so that it only binds to Casp9 and not to Casp3. This wouldin fact destroy causal connections exiting X0. Second, it may not be possible toadequately control for variables along alternative chains, in this case C30. An in-tervention holding C30 fixed is most likely to neutralise the effect of C90 on C31,thereby making C31 independent of C90. This is because, at the tiny timescalesrequired to recover the Markov condition in the DBN, there is arguably no surgi-cal intervention that modifies Casp3 “instantaneously”, that is, without holding itfixed for too long at one time, thereby setting its value at subsequent times, too,and preventing Casp9 to have a say on the matter.

Notice that there are alternative ways of unrolling a cyclic graph, dependingon what relations are included in the prior network. Not all choices are equallygood. For the purpose of illustration, I assumed that the prior network in figure6 satisfies best the Markov condition. However, my argument need not rely onthis assumption. In complex structures such as this one, any alternative choicewould generate analogous problems. Intuitively, the problem is avoided if thetested chain is in the prior network, which is a DAG. But since the original causalgraph is cyclic, there is no way to have all chains in the prior network. Actually,

25

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any DBN can include at most one out of the four chains involving X, C9 andC3. Thus, for any given choice at least three chains may not be testable withoutviolating modularity. For instance, consider the alternative DBN in figure 7. Here,interventions on X → C9 → C3 do not violate modularity. But interventions onall other chains (C9→ C3→ X, C3→ X → C9, and X → C3→ C9) do.

Nonetheless, the proposed solution grants to the analysis of the mechanismthe required explanatory autonomy. One may in fact be able to calculate the inde-pendent contributions along the paths and use them for counterfactual reasoning.And one may analyse causal relations in mechanistic terms, by mentioning thedirected paths between cause and effect and the relevant probabilities. Thus, theDBN provides, at least in this case, a solution to the explanatory regress, and amechanistic analysis in the spirit of Glennan’s account—although not in virtue ofGlennan’s mechanism-intervention symmetry thesis.

However, one should not overestimate this result. The proposed solution mightnot apply to all mechanisms, and thus not constitute a general mechanistic accountof causality. This holds even of cyclic mechanisms, which DBNs are explicitlydesigned to accommodate. Some cycles are not Markovian or stationary. For in-stance, Casp9, like Casp3, contributes to free XIAP. For the sake of simplicity,I ignored the C9 → X causal connection. If this assumption is not justified, theresulting process is not Markovian, since there is one chain whose dependencesspan over two time periods, so that not all variables at t + 1 are probabilisticallyindependent of those at t − 1 conditional on those at t. In turn, relaxing the as-sumptions that the process is Markovian and stationary is possible but comes atthe cost of added computational complexity. Also, some cycles are static ratherthan dynamic. In such cases, the cycle results in an equilibrium that is best repre-

26

sented by d-separation, rather than by a DBN (Clarke et al., 2014). For instance,the apoptotic mechanism may be approximately stationary only for some time.When equilibrium is reached and the cell is dead, the causal structure will havechanged so as to render the DBN inadequate. Besides, scientists often talk ofcausal relations across levels. The RBN formalism was put forward to modelinterlevel causal relations (Casini et al., 2011) and later extended to modellingcycles (Clarke et al., 2014). Neither Glennan’s account nor DBNs by themselves,though, have the resources to analyse interlevel claims such as “XIAP causes theirreversibility of Casp3 activation”.28

In addition, many other formalisms seem suitable to represent mechanisms(Materi and Wishart, 2007): differential equations, which are for instance morenaturally suited to model feedback systems in a continuous fashion; neural net-works, which can model arrows that adapt their weights through learning; Petrinets, which can better represent nondeterministic biochemical reactions; agent-based models, which can represent the dynamics and interactions of objects inspace and time; etc. And we have a plurality of qualitative representations, too,also useful for counterfactual reasoning involving mechanisms. This is evidentif one attends to the (apparently irreducible) diversity of graphical languages forrepresenting biological mechanisms (see, e.g. Le Novere et al., 2009). Amongall these different representations, arguably some are better suited for one kindof systems, others for other kinds of systems. The analysis of mechanism af-forded by such representations can vary from case to case—it surely does in theaforementioned cases of modular, dynamic-cyclic, static-cyclic, and hierarchicalmechanisms. It is still an open question whether there is a unified analysis of thenotion of mechanism—for instance, whether the RBN formalism can be extendedto model non-stationary processes, spatial information, etc.

All this, in turn, has the following two consequences. First, spelling out howthe different notions of mechanism relate to causality—which requires further cri-teria besides interventions—may be a more challenging task than the mechanistappreciates. Unless this task is accomplished, one cannot claim to have vindicatedthe explanatory autonomy of all higher-level causal claims, and thus to have anaccount of causality. Secondly, even if a connection is established between eachnotion and causality, what one might get is at most an explication of “causality”in terms of a cluster of several analyses of “mechanism”, which may have little

28This means that, strictly speaking, the DBN solution is only partial. It deals with modularityfailures but provides no explicit analysis of the causal claim established by Legewie et al. (2006),since irreversibility is higher-level with respect to XIAP and Casp3.

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to do with one another. This is by itself compatible with regarding each kindof mechanism as a different kind of evidence for causality29, which is not quitethe same as holding that causality is constituted by mechanisms. The mechanistmay want to insist that the cluster is somehow unified because all its members arecalled ‘mechanisms’. But then the mechanist owes us a story on how the variousanalyses are reducible to one another, or at least some strong analogy may be es-tablished among them.30 Otherwise, there is little difference between endorsinga mechanistic account of causality and embracing some form or other of plural-ism (Cartwright, 2004; Longworth, 2006; Reiss, 2011). Unless this further task isaccomplished, one cannot claim to have vindicated the explanatory autonomy ofhigher-level causal claims on mechanistic grounds, and thus to have a mechanisticaccount of causality.

8 The prospects of Glennan’s accountLet me recap my argument so far. Glennan’s recent appeal to interventions isaimed at rebutting two objections, one from Psillos regarding the ontologicalasymmetry between mechanisms and counterfactuals, one from Craver regardingthe explanatory regress the mechanistic account runs into. Of the two replies, I ar-gued, the first misses its target: the manipulationist account just isn’t subject to theontological regress that vitiates the mechanistic account. I have shown that if onegives up the ambitious task of explicating the nature of the necessary connection,interventions may still be applied, and more fruitfully so, to address the secondobjection, by justifying the explanatory autonomy of mechanisms. However, thesuccess of this move is restricted to the domain of modular systems. For instance,it fails in the case of certain cyclic mechanisms, as illustrated by the mechanismfor the irreversibility of apoptosis. More generally, this casts doubts on attemptsto account for mechanistic explanation with the aid of manipulationism.

Two paths seem open to Glennan. Either he restricts his account to modu-lar systems or his account is incomplete. In the former case, if the account is to

29This is in line with Russo and Williamson (2007)’s evidential pluralism, although the kindsof evidence they distinguish are difference-making evidence and mechanistic evidence, and notdifferent kinds of mechanistic evidence.

30The analogy may not be itself mechanistic. Russo and Williamson (2007) hold an epistemicview, where the various kinds of evidence belong to the epistemology of an omniscient agent.Casini (2012) holds an inferential view, where the various kinds of evidence are united by theirsimilar inferential role in scientific practice.

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retain a wide scope across all special sciences, mechanistic explanations in non-modular systems should be banned as illegitimate. This option looks undesirable.In the latter case, instead, Glennan should spell out the connection between non-modular mechanisms and causality. I propose a solution based on interpreting“mechanism” in terms of probabilistic dependences in a DBN rather than in termsof interventions. This, at least in the case of the proposed counterexample, vindi-cates the explanatory autonomy of the mechanism, and explicates “causality” interms of “mechanism”. At the same time, I noted, the mechanist owes us a storyon how “causality” is explicated by “mechanism” in cases where neither inter-ventions nor DBNs apply. Adequate analyses of “mechanism” must be providedin such cases, as well as some argument on why the notion of mechanism unifiesthe various analyses. So, even if Glennan’s story is illuminating—which I think itis to an extent—and even if the sort of counterexample I illustrate is successfullyhandled by refining the story, we are still a long way from the whole story, that is,from a mechanistic account of causality.

Funding Deutsche Forschungsgemeinschaft (‘CAUSAPROBA’, Sp 279/15-1); Alexan-der von Humboldt Foundation; Swiss National Science Foundation (‘Grounding – Meta-physics, Science, and Logic’, CRSII1 147685/1).

Acknowledgments I am grateful to Stuart Glennan, Catherine Herfeld and Julian Reissfor comments on previous versions of the paper. I also thank Michael Baumgartner, DavidDanks and Marcel Weber for very helpful discussions.

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