CAUSALITY, MECHANISMS AND MANIPULATION
Maria Carla Galavotti
Department of Philosophy
University of Bologna
e-mail: galavott@philo.unibo.it

This paper suggests an integration of Wesley Salmon=s mechanistic theory of causality with a manipulative account of causation of the kind that has been recently defended by Huw Price and Peter Menzies. Firstly, Salmon=s view of causality is outlined, and the main issues of the debate around it are recollected. Secondly, the manipulative view of causality is sketched and the possibility of its integration with Salmon=s theory is considered for the purpose of coping with some of the problems raised by its critics. 

1. Two kinds of probabilistic causality.
After having been for centuries an essential component of the mechanistic picture of the world, where is strictly connected to explanation, causality underwent a crisis after the deterministic paradigm was put in doubt by the new physics. Not surprisingly, causality plays a minor role within Hempel=s model of explanation, which has long been considered the official theory of scientific explanation developed by philosophers of science, ever since it was put forward in the early forties.
  The resurgence of causality after a period of disgrace is linked to its probabilistic interpretation. The literature on probabilistic causality started with a few important albeit isolated works, like Hans Reichenbach=s The Direction of Time  (1956), Irving John Good=s AA Causal Calculus@ (1961-62) and Patrick Suppes= A Probabilistic Theory of Causality (1970), and has been constantly growing since. The probabilistic treatment of causality leads immediately to a distinction between causal talk referring to population variables, or Aproperty causality@, and causality between single events, often called Atoken@ or Aaleatory@ causality. 
  The difference between the two was recognized at the outset by Good, who distinguished between Athe tendency of F to cause E@ and Athe degree to which F caused E@ (Good 1961, p. 307). For Good, these are different notions, resting on different probabilistic assumptions and requiring different probability measures. 

  In his monograph on probabilistic causality, Suppes proposed two definitions, one in terms of events and another in terms of statistical variables. However, it is clear that he was thinking of kinds of events, or property causality. As a matter of fact, most of the criticisms and counterexamples raised against Suppes= theory of causality are (explicitly or implicitly) centred on the lack of distinction between two different levels of analysis. In reply to his critics, Suppes came to an explicit recognition of two different kinds of causality. So much granted, he favors a theory of probabilistic causality referred to kinds of events, claiming that what matters in science is the behaviour of populations, more than that of single events (see Suppes 1984). 
  The tension between property and token causality reflects the tension between prediction and explanation in probabilistic contexts. Property causality has predictive power, but differs from predictability, since one can usually make predictions based on mere statistical correlations. On the contrary,  single events are often unpredictable, and can only be explained after they occur. The information at our disposal is generally based on knowledge regarding statistical relationships, and these do not bear directly on explanation. This is why Suppes= theory is not intrinsically linked to explanation, and Good treats the notion of Aexplicativity@ independently from that of causality. 

2. Salmon=s mechanistic view.
A major attempt to put together explanation and probabilistic causality has been made by Salmon, who grounds the notion of explanation on that of causality, in the conviction that genuine explanation is causal in character, and that explanatory power rests on causal links. Salmon=s aim is to revive mechanical explanation in a probabilistic framework.

  His first attempt to do this is contained in Statistical Explanation (1970). Building on the notion of statistical relevance, he put forward a model of explanation meant as an alternative to Hempel=s approach. This is the Statistical-Relevance model of explanation, according to which an event is explained by showing what factors are statistically relevant to its occurrence. To accomplish this aim, the reference class to which the event belongs is gradually restricted, to make it include only the relevant factors. In this process, irrelevant factors are Ascreened off@ by relevant ones until a homogeneous reference class is obtained. The fact to be explained is eventually located in a network of statistical relations holding between the properties that are relevant to its occurrence. One can say that the explanandum event is explained when its place within such a network is specified. In other words, explanation associates the event to be explained with a probability distribution. 
  When referred to a homogeneous reference class, the probability distribution associated with the explanandum reflects the most complete and detailed information attainable. Homogeneity of the reference class then becomes the main requirement of explanation conceived in this way. However, given the obvious difficulties connected with the notion of Aobjective homogeneity@, Salmon takes it as characterizing the ideal case to which explanations should tend. In those cases in which objectively homogeneous reference classes are not obtainable, epistemically homogeneous reference classes are adopted. 
  Clearly, the S-R model conveys information on the statistical relationships holding among the variables of a population. But for someone like Salmon, who thinks that explanatory information has to be causal, statistical correlations themselves call for an explanation. In view of this, Salmon works out a second level of explanation with respect to which the S-R model becomes a first step, also called AS-R basis@. Explanation of the second level is causal and includes a probabilistic theory of causality.
  To substantiate probabilistic causality, Salmon adopts the notion of Acausal process@, defined as a spatio-temporal continuous entity having Athe capacity... to transmit information, structure and causal influence@ (Salmon 1994, p. 303). Causal processes are responsible for causal propagation and provide the links between causes and effects. They can be thought of as forming a net, whose knots represent interactions between processes. When processes are modified in such interactions, causal production takes place. The changes thus obtained persist after the interaction and are propagated by the processes. The basic idea that enters in the definition of causal nets is the Aprinciple of the common cause@ which Salmon borrows from Reichenbach with some modifications, mainly due to the fact that in a causal interaction the common cause does not Aabsorb@ the probability of the joined effects. (Instead of the equality P(A @ B³C) = P(A³C) @ P(B³C), we have the inequality P(A @ B)³C > P(A³C) @ P(B³C).) 
  Causal processes and interactions, however, cannot be defined only in terms of relationships among probability values. According to Salmon, speech in terms of causal processes and interactions makes reference to physical properties, which form the mechanisms responsible for the occurrence of phenomena. Causal explanation obtains when phenomena are located at some point within the net of causal processes, and it tells us how such mechanisms work. Salmon=s theory of causal explanation is contained in the volume Statistical Explanation and the Causal Structure of the World (1984), which has provoked extensive debate.

  This revolves partly around the notion of Acausal process@, beset with various difficulties pointed out, among others, by P. Kitcher and P. Dowe. Their main criticism regards the appeal to counterfactuals contained in Salmon=s first formulation of processes in terms of Amark transmission@. After having amended it a few times, Salmon proposed a formulation in terms of Ainvariant quantities@ (Salmon1994) and more recently one in terms of Aconserved quantity@ (Salmon 1997). The issue is under debate.
  A further topic of concern is the relationship between the two levels of explanation envisaged by Salmon. After the publication of (1984), in a series of papers now included in the collection Causality and Explanation (1998), Salmon gradually reached the conclusion that the two levels of analysis, namely the S-R basis (or Astatistical causality@) and causal talk in terms of processes and interactions (or Aaleatory causality@) cannot be brought together. Let me briefly recollect how Salmon reached this conclusion.
  Around 1990 Salmon thought that only aleatory causality could give Aan adequate understanding of causality@ (Salmon 1998, p. 207). This conviction has been challenged by C. Hitchcock on the claim that, while envisaging a geometrical network of processes and interactions, Salmon=s theory does not contain any hint as to what properties should be taken as explanatory. In contrast, Hitchcock favours a stronger notion of explanation, and endorses Woodward=s claim that explanation answers Awhat-if-things-had-been-different@ questions. Following this path he is led to the conclusion that Aa successful account of explanation had better make the relation of explanatory relevance look roughly like that of counterfactual dependence@ (Hitchcock 1995, p. 311), and that Aour demand that explanations provide relevant information requires something stronger - that we be told which earlier properties the properties specified in the explanandum depend upon@ (ibidem). On this basis Hitchcock  rejects Salmon=s distinction between two levels of explanation and claims that explanatory relevance should rest on the counterfactual information given by statistical correlations. 
  In response to Hitchcock, Salmon has reconsidered the link between the two levels of explanation, coming to the conclusion that A(1) statistical relevance relations, in the absence of connecting causal processes, lack explanatory import and (2) that connecting causal processes, in the absence of statistical relevance relations, also lack explanatory import... Both are indispensable@ (Salmon 1997, p. 476). The two levels of explanation are thereby strictly connected. The causal model in terms of processes and mechanisms is accordingly seen as an essentially geometrical model, that needs to be implemented with information on statistical relevance relations in order to allow recognition of those properties which are pertinent to given outcomes. 

  At the same time, Salmon concedes that counterfactual considerations have a role to play within explanation and reaffirms a close connection between statistical relevance and counterfactual information. After banning counterfactuals from the definition of causal processes, he admits them at the explanatory level, claiming that such counterfactuals are Arelatively unproblematic@, being supported by well established statements of statistical correlations, based on observed frequencies. The question of a more detailed definition is not addressed. 
  As a further concession to his critics, Salmon recognizes that pragmatical considerations have a role in determining what is to be taken as explanatorily relevant. This move is made necessary by the fact that causal talk in terms of processes can be conducted at different levels of analysis. Sometimes facts can be accounted for at the most detailed level allowed by scientific theories, but in most cases phenomena are analysed at different levels of abstraction, as determined by the context. 
  The far from straightforward story of the development of Salmon=s theory testifies the difficulties one encounters when trying to combine property and token causality within a unified theory of (probabilistic) explanation and prediction. In Salmon=s view the asymmetry of causation is made to depend ultimately on spatio-temporal continuous processes, and such a notion is much too strong to guarantee the model wide applicability. The same holds for the homogeneity requirement, which usually does not apply to the social sciences. This is stressed by J. Woodward, who observes that in this field variables like class, religion, education and the like are taken as explanatory even though finer partitions would be possible (Woodward 1989). Though many agree that Salmon=s theory captures our intuitions about explanation and causality, as well as important features of explanation in classical and statistical physics, its application to other fields encounters major difficulties. 
  In (Galavotti 1999) I suggested a possible integration between Salmon=s model and a weaker notion of causality, like the manipulative view that has been worked out in some detail by the literature on econometric models. A similar notion of causality has recently been the object of a perceptive treatment by the epistemologist by H. Price, partly in collaboration with P. Menzies.

2. The manipulative view 	

The central idea of Price=s approach is that, instead of being taken as a property of the world, causality can be related to the agent=s perspective in view of the fact that we acquire the notion of causation through our experience as agents. ARoughly, to think of A as a cause of B is to think of A as a potential means for achieving (or making more likely) B as an end@ (Price 1992a, p. 514). The main advantage of this view is that it embodies in a natural way the asymmetry of causation, which derives from that characterizing the means-end relationship. In Prices= words: Acauses are potential means, on this view, and effects their potential ends. Causal asymmetry originates in our experience of doing one thing to achieve another; in the fact that in the circumstances in which this is possible, we cannot reverse the order of things, bringing about the second state of affairs in order to achieve the first@ ( p. 515). Price ascribes the paternity of this conception to F.P. Ramsey, who thought that like other notions, including probability, laws and chance, causation is ultimately rooted in the perspective with which an agent looks at the world (see Price 1992b). 
  The general formulation of the agency theory of causation given by Price and Menzies is probabilistic, but they add that Ait seems reasonable to expect that an indeterministic theory will incorporate the deterministic notion, as a special or limiting case@ (Menzies and Price1993, p. 189). The means-end relation on which the theory is grounded is characterized in terms of what they call Aagent probabilities@. The latter Aare to be thought of as conditional probabilities, assessed from an agent=s perspective under the supposition that the antecedent condition is realized ab initio, as a free act of the agent concerned. Thus the agent probability that one should ascribe to B conditional on A...is the probability that B would hold were one to choose to realize A@ ( p. 190). 
  Though this position is admittedly anthropocentric, and takes causality to be a context-dependent, according to its proponents it has decisive advantages over more traditional approaches. The fact that it accounts in a natural way for causal asymmetry, which is embedded in the notion of agency, has two important consequences. First, in this perspective the arrow of causation does not need to be based on the arrow of time. For Price this is a major advantage, as it allows for contemporary and even backwards causation, which plays a decisive role in his interpretation of microphysics (see Price 1996).
  Furthermore, the agency view offers a solution to the problem of spurious causation, because it assigns statistical correlations an asymmetrical interpretation in the light of the agent=s (probabilistic) judgment in terms of the means-end relation. So, according to Menzies and Price, AAgent probabilities fail to generate spurious correlations because they abstract away from the evidential import of an event, in effect by creating for it an independent causal history. For example, in enquiring whether one=s manipulation of an effect B would affect the probability of its normal cause A, one imagines a new history for B, a history that would ultimately originate in one=s own decision, freely made. And one thereby deprives B of its usual evidential bearing on A. The same is true, mutatis mutandis, for the situation in which one manipulates one effect of a common cause to see whether it makes a difference to the probability of another effect@ (Menzies and Price 1993, p. 191). From the point of view of the agent, correlations due to common causes do not necessarily translate into probabilistic dependencies. Consequently, from the point of view of the agent there is no divergence between probabilistic and causal relevance, they just coincide. 

  An additional advantage of the agency theory of causation is that of being a good substitute for the counterfactual theory. In this connection, Price argues that the agency theory not only conveys the same sort of counterfactual information conveyed by Lewis= theory, but it does it in a less controversial way, because it does not need any postulate on similarity among possible worlds, and the like (see Price 1991).
  In order to accept the agency theory one has to regard human beings as actors, not as pure observers. Therefore, this approach is not purely humean, but instead of appealing to non-humean concepts which are modal or metaphysical in character, it embraces a pragmatist perspective which makes causality depend on the agent=s knowledge and beliefs. However, as observed by Price, looking at causality as perspectival in character, or as Aa manifestation of the fact that causal concepts originate in our experience as agents@ (Price 1992a, p. 501), does not put it beyond the reach of science, nor does it rule out a realist account of causation, if one wants it. A realist, who wanted to interpret observed correlations as expressions of objective causal relations existing in the world, could still accept the agency view on the claim that agency is not to be taken as a constituent of the world, but rather as Awhat makes causation accessible and important for agents@ (Price 1991, p. 173). Even taking for granted that there are objective causal relations in the world, the realist should be ready to admit that Aas agents in the world, we are capable of exploiting these relations to further our ends@ (p. 172). 
  Although the agency view does not conflict in principle with a realist explication of probabilistic causality, according to its proponents it Adoes serve to undermine a popular recent argument for causal realism@ (Price 1991, p. 161), namely Cartwright=s plea for non-humean causality, which also appeals to some form of agency (effective strategies). In contrast, Price claims that Agiven the constraints of the agent=s perspective, ordinary procedures of evidential reasoning can draw the distinctions they are said to be unable to draw. The distinction between effective and ineffective strategies needs evidential reasoning by agents, and nothing stronger@ (ibidem). In other words evidence, and in particular experimental relative frequencies, guide the agent=s choice of those actions which, in the light of past experience, are effective strategies for achieving certain ends. 

  The perspective outlined by Price and Menzies provides a general philosophical framework for agency causality. In order to become a flesh and blood theory of causality, it has to be substantiated by more specific accounts. The literature on econometric model building offers a number of such, ranging from the classical approach developed by H. Simon and H. Wold - which takes causality in a manipulative sense and defines it with reference to (deterministic) models - to more recent accounts where models are specified in terms of statistical variables. In both cases causality  is associated with exogeneity and some sort of invariance. In non-experimental disciplines like econometrics, characterized by a weaker theoretical status than physics, causality acquires a peculiarly pragmatical character. Models are typically context-dependent constructs, and their specification depends on  theoretical assumptions underlying the choice of exogenous variables, as well as on the nature of the available data. In the case of statistical models, other elements come into play, like the expectations of economic agents. In addition,  the specification of an econometric model usually depends on the purpose for which the model is to be used. 

3. Towards an integration
Let us examine the possibility of combining Salmon=s mechanical approach with the agency theory of causation. A first advantage of such an integration would amount to the fact that causal talk would receive counterfactual import in a straightforward and noncontroversial way.
  In spite of Price=s claim that the agency theory of causation can be combined with a realistic interpretation of the correlations involved, the introduction of the agency theory within Salmon=s approach brings with it a pragmatic flavour. This does not concern only the introduction of contextual considerations into causal analysis, recently accepted also by Salmon. The pragmatical character of agency causation involves an equally pragmatical interpretation of laws and theories, of the kind reaffirmed by Ramsey. In his commentary to my AWesley Salmon on Explanation, Probability and Rationality@ (Galavotti 1999), Salmon claims to be ready to accept an Aepistemic approach to theories and laws@ because he thinks that this Awould not necessarily undermine the ontic status of causality@ which he wants to retain. However, he also regards convergence of opinion, which is the criterion usually advocated by pragmatists to substantiate scientific truth, as too weak for a realist like him. 

  The Aontic status of causality@ advocated by Salmon rests on the specification of causal mechanisms and the idea that homogeneous reference classes can be obtained. Even granted that in some fields this can be done, there are many areas where information on homogeneous reference classes cannot be reached and/or causal processes cannot be devised. A manipulative view of causality would be of some help in such cases, allowing recognition of causal properties even when the homogeneity requirement is not fulfilled. So, under this interpretation Salmon=s theory would find an easier application to such cases like those pointed out by Woodward in connection with the social sciences and mentioned above. If this were true, the combination of Salmon=s model with a manipulative theory of causation would widen its range of application.
  Another point stressed by Woodward is that in the social sciences the sought explanation of phenomena usually does not regard single case occurrences of events, but rather facts at the population level. This is testified by the extensive use of statistical techniques for regression analysis, which convey the kind of information that is typical of the S-R basis. According to Woodward, this is a further argument against the suitability of Salmon=s mechanical approach to the social sciences. 
  These remarks bring us back to the problem of the two levels of causality. To a certain extent, the impact of this problem varies according to the context in which it arises. The mechanistic picture of causality advocated by Salmon suits disciplines like classical and statistical mechanics, which are characterized by a strong theoretical apparatus and by the use of general laws to describe the phenomena. In these fields, provided the available information allows identification of causal mechanisms, statistical causality will match token causality. In other disciplines, characterized by a weaker theoretical apparatus, the description of phenomena rests on models and the linkage between statistical and token causality, as well as that between explanation and prediction, becomes looser. This is the case with the social sciences and econometrics. So, within different scientific contexts the two kinds of causality stand in a different relation to each other.  
  As Price is aware, embracing the agency view is not sufficient to solve the problems connected to the existence of two levels of causality. However, Price holds the conviction that the agency approach can help to clarify some aspects of such problems. We are led to an analogous conclusion by the literature on econometrics, where the shift between population and token causality acquires a peculiar significance, being linked to the problem of aggregation. In broad terms, this amounts to the problem of connecting the behaviour of single agents (at the level of microeconomics) to the behaviour of the so-called Arepresentative agents@ (at the level of macroeconomics). With reference to causality, the problem is that of combining the causal structure of the microeconomic model, where exogenous variables are said to causally influence the endogenous variables, with the interdependence among the variables of the macroeconomic model, where the behaviour of each representative agent is simultaneously dependent on the behaviour of the others. 

  In his ACausality and Temporal Order in Macroeconomics or Why Even Economists Don=t Know How to Get Causes from Probabilities@ (1993) Kevin Hoover makes this problem the starting point of an attack against accounts of causality based on temporal order, in particular Cartwright=s approach. He claims that what is needed instead is a manipulative approach to causation, and advocates Simon=s view that Acausal ordering is invariant to well-defined classes of interventions@ (Hoover 1993, p. 706). He also conjectures (without developing the argument) that Simon=s account Abears a family resemblance to Salmon=s view that a causal process is one that will transmit marks@ (ibidem). Actually, such an analogy does not seem to go a long way, beyond the fact that both accounts appeal to some sort of invariance. Between the two approaches there is a fundamental difference, lying in the deterministic nature of Simon=s approach to causality, where models are not specified in terms of statistical variables. 
  The last two decades have seen an enormous outgrowth of econometric models specified in probabilistic terms. While retaining the manipulative notion of causality, they face peculiar problems with regard to the aggregation problem. The situation can be summarized as follows. In econometrics it is usually assumed that a particular economic agent, the policy maker, tries to influence the behaviour of other agents by manipulating the value of some exogenous variables, usually called instrument variables. Now, while it can be assumed that such variables are strictly causal with respect to single agents, in the sense that single agents cannot influence their value, at the aggregate level the behaviour of all agents can have feedback effects on the instruments of the policy maker, thereby altering the causal structure of the effect. In view of this, it is necessary to introduce a conditional concept of manipulability, according to which the efficacy of the intervention of the policy maker is subordinate to the body of information available to economic agents. 
  In general, the heterogeneity of individuals and the uncertainty due to their interaction on the market make it impossible to pass from individual to aggregate behaviour in any simple way (like summing up). Assuming that heterogeneity and uncertainty can be described by means of probability distributions, models will have to be specified in terms of relations between the expected values of such distributions, conditional on the body of information available to economic agents, where information varies not only over time, but also according to the chosen level of aggregation.

  The conclusion attained by the literature on econometrics is that the relationship between causality referred to statistical variables and causality referred to individuals can be handled only at the price of introducing a conditional notion of manipulability and a dynamical specification of causal structure, dependent on the expectations and the information available to economic agents, as well as on the purpose for which models are used. Clearly, the nature of the Aindividuals@ forming the object of econometrics, namely economic agents, is at least in part responsible for the necessity of weakening even the notion of agency causality, in the sense suggested above. What happens in other fields dealing with natural phenomena is a matter for further investigation. The problem of connecting the two levels of causality remains open. Though embracing the manipulative view is not enough to solve it, its adoption might still help to clarify the matter, as seems to be the case with econometrics. 
  Salmon=s causal mechanisms based on homogeneous reference classes and having an ontic status look quite distant from the pragmatical and context dependent notion of causation linked with manipulation. However, the agency approach is flexible enough to accommodate stronger accounts, including Salmon=s. In order to mend the fracture between the two approaches one would have to start from the weaker framework of the agency theory of causation, and keep the mechanical picture as an ideal. When scientific theories allow causation to be specified in terms of mechanisms, that ideal will be fulfilled, otherwise manipulability will suggest weaker accounts of causation. Still, the mechanistic picture can be retained as a heuristic device.  

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