Causation in the Social Sciences: An...

Causation in the Social Sciences: An OverviewAuthor(s): Paul HumphreysSource: Synthese, Vol. 68, No. 1, Causality in the Social Sciences (Jul., 1986), pp. 1-12Published by: SpringerStable URL: https://www.jstor.org/stable/20116292Accessed: 23-05-2020 00:56 UTC

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PAUL HUMPHREYS

CAUSATION IN THE SOCIAL SCIENCES: AN OVERVIEW*

Consider a typical causal claim made in the social sciences. The claim will ordinarily be about a factor which is but one of many influences on a given phenomenon, and each of those multiple causal influences will be stochastically, rather than deterministically, linked to its effects. The causal factors will usually have multiple effects, and the context within which they are operating will frequently be a nonexperimental one, often necessarily so. The causal claim will be directly about relation ships between variables, and only derivatively about events. Finally, those variables will, in many cases, correspond to latent dispositions which are observationally accessible only via a theory of measurement which itself will often be in the form of a causal theory, in which multiple observable indicators are linked to the underlying latent dispositions. If one then compares these causal claims with traditional analyses of causation which are available in the philosophical literature, those analyses begin to look almost willfully nondescriptive. One could simply dismiss the causal claims that are made in the social

sciences as false, or at best misguided, and reject along with those claims the explanations and social policies which have been constructed on the basis of them. But such a dismissal would be much too hasty, because there are important lessons for the philosophy of science to be learned here. The nonprobabilistic philosophical analyses of causation are especially deficient in contexts where multiple stochastic influences are present, whereas the methods which have been developed in the social sciences for discovery and validation of causal relationships have been painstakingly worked out with explicit recognition of the pitfalls inherent to the area, and thus provide a valuable resource for the investigation of the properties of complex causal systems. The purpose of this introductory essay is to lay out some of the basic concepts underlying theories of causality in the social sciences, to indicate some unifying elements which bind together the articles which follow, and to suggest where some useful connections with the existing philosophical literature may be made.

Synthese 68 (1986) 1-12. ? 1986 by D. Reidel Publishing Company


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1. NONEXPERIMENTAL METHODOLOGY

Rather than characterizing the social sciences on the basis of their subject matter, it is at least as fruitful to start with the fact that most social phenomena are not amenable to investigation by laboratory experimentation. Indeed, as Cook and Campbell point out, although the use of nonlaboratory designs is often forced on the investigator, the move outside the laboratory is sometimes deliberate because restric tions on the sample size and the length of the study, for example, frequently make the conclusions from laboratory investigations seriously inadequate. One can, in some cases, use randomized experi mental designs to investigate causal relationships, but (for reasons also detailed by Cook and Campbell) such designs are often not appropriate. Thus the investigator must either postulate and test a number of hypothetical causal models, or employ the techniques of quasi-experi mentation.

In the case of causal models, the characteristics of social phenomena which occur in natural settings inevitably affect the form of theories designed to cover them. The presence of multiple causal influences requires that the theories be multivariate in nature. Both the un controllability and our ignorance of many of those multiple influences

means that the effects of those factors which are included in the analysis will affect the outcomes in a probabilistic, rather than a deterministic, fashion. Many of the factors involved in causal relationships will be described in a relatively abstract way, in order to achieve some fair degree of generalizability across varied contexts, and hence will be only indirectly accessible to observation. Finally, those causal factors will ordinarily be quantitative in form, and measured on an interval or ratio scale to allow the use of covariance measures* as the basis of statistical

analysis and interpretation. With these requirements in mind, we can briefly show how the core

elements of most of the techniques generically known as causal modelling can be motivated. Consider these three elementary assump tions:

Al The value of a variable Y is the sum of a traditional deterministic functional of the variables X1,..., Xn and of a stochastic disturbance term C/, i.e.,

Y = f(Xl9...,Xn)+U


CAUSATION IN THE SOCIAL SCIENCES 3

A2 The causal contribution of a variable X? is the same what ever value the other variables Xy- happen to have.

A3 The functional / is that which, among those satisfying the constraints Al and A2, minimizes the indeterminism in the system.

It is then possible to show that under assumptions Al, A2, and A3, plus an auxiliary assumption of linearity, that

Y= E(Ylxu ..., Xn) + U = i aiXi + U ?=o

for all values (jci,..., xn) of (Xu ..., Xn). (A discussion of the motiva tions behind these assumptions, and a derivation of the result given above can be found in Humphreys (1986).)

Such linear structural equations represent the causal influences of a number of exogenous variables Xi,..., Xn on the endogenous variable Y, and bear some resemblance to multiple regression equations. But because endogenous variables can themselves affect other variables, and changes in the values of exogenous variables often result from changes in other variables, we want to embed the structural equations within a network of similar equations. Such a network is then called a causal model. With additional assumptions, one can arrive at recursive systems (where there is no feedback between causal variables, so that a hierarchical structure can be constructed) or, if the causal relations are between unobservable (latent) variables, a causal theory of measure ment can be added to the underlying structural model to arrive at multiple indicator models. I shall not discuss these aspects in detail, for they form part of the subject matter of the papers by Blalock and by Glymour and Scheines, and are well described there.

2. GENERALIZABILITY, INDUCTION, STRUCTURAL STABILITY, AND SIMPLICITY

The peculiarities of working within nonexperimental contexts pose two immediate problems for those who wish to construct theories of some generality. The first is that generalization from experimental findings to field settings will often be problematical, and the second is that generalizing from one nonexperimental context to another will also have to be made with some care. It is difficult to address these issues


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within the usual philosophical approaches to inductive inference because for most of those approaches, the origins of the empirical data which form the basis of the inductive inference are not a relevant aspect of the inference, and hence changes in the generating conditions of the data are not included in the analysis. This disregard for the structural aspects of specific systems stems from two sources. The first is the atomistically oriented empiricism of most humean analyses of causa tion. Hume himself claimed that inductive inferences are based upon cause-effect relations, but focussing on discrete sequences of observ able events makes reference to permanent or semi-permanent struc tures underlying the phenomena difficult. The second source of this lack of concern for the concrete is the natural desire to deal with inductive issues at the highest level of abstraction, making the criteria of validity as subject-independent as possible. This approach leads naturally to a heavy emphasis on logical theories of confirmation, the use of completely abstract theories of statistical inference, and (stem ming from the first source) a requirement of using relative frequencies as the only acceptable interpretation of objective probabilities or probabilistic causality. There are two features of causal models which are at odds with this

kind of tradition. It is presumed within the models that the structural coefficients in the linear equations are constants, and in particular, that they will remain invariant both across changes in setting from experi mental to field settings, and cross-temporally within the same setting. In experimental settings, this stability is artificially achieved by isolation of the system and control of the residual and exogenous variables and hence the presumption is rarely mentioned. But in nonexperimental contexts, the invariance assumption must be explicitly made, and its frequent failure is one reason why generalization across different contexts is so difficult in the social sciences, and why direct in vestigation into specific contexts by nonexperimental methods is often used. Also, as Blalock points out, similar requirements of stability must be required of the measurement theory as well, and whereas this is ordinarily an unproblematical assumption for physical theories because of the simplicity and robustness of their measurement theories, it is nothing like as simple for the complex measurement theories of the social sciences. These generalizability issues have an important consequence, for

theories of induction often place a premium upon induction to the



simplest theory consistent with the data. But as Blalock argues, there is an unresolvable tension between the desire for parsimony in social theories, the need for generalizability, and the goal of precision, and the rapid increase in complexity in social theories as the number of measured and latent variables is increased to obtain greater accuracy leads to restrictions being placed on the generalizability of the resulting theories. Furthermore, an emphasis on present simplicity may often seriously compromise the future accuracy of a theory. Thus the widely held view that of two empirically equivalent theories, the simpler is to be preferred is a view which cannot be applied without these further considerations being taken into account. In fact, Blalock concludes that sociology as a subject is in serious danger of becoming a fragmentary discipline because of this tension between manageable degrees of complexity and desired broadness of scope of the theories. A partial solution to this problem of complexity is provided in the

paper by Glymour and Scheines, as a result of their view that the complex theories of the social sciences are an ideal subject for the resources of artificial intelligence. A principal methodological tenet they use requires that for a theory to be accepted it must not only fit the available data, but fare better in that respect than do competing theories. That is, even though a theory may account for the data to an acceptable degree, there may well be an alternative model which will do even better. Because of the huge number of causal models which are possible with only a few variables under consideration, not to mention the rapid increase in complexity of the theories themselves as the number of variables is increased, this is an area in which the use of computer-assisted science is a natural strategy. They thus provide a synopsis of the rationale underlying their TETRAD program, which is designed to search for and compare alternative latent variable models, together with some examples of the successful application of this approach.

A further consequence of considering causal models is the effect they can have on our attitude towards statistical methodology. Statisticians have always insisted that mere post-hoc analysis of data is highly undesirable, and integration of statistical methods with experimental design and substantive scientific theory is a requirement for informative work in this area, and certain aspects of causal models clearly indicate the need for such integration. For example, in order for the use of ordinary least squares to be justified, the error terms in the structural


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equations have to be uncorrelated. Also, as Blalock notes, the fact that estimates of the structural coefficients are unaffected by whether the error terms are fixed or allowed to vary depends upon the assumption that they affect only a single variable. Duncan's paper, too, is relevant in this regard. Philosophers have

sometimes doubted whether the use of propensities rather than relative frequencies makes any real difference to the statistical methods used. One of the principal morals to be drawn from Duncan's article is that treating models in terms of individual propensities rather than popu lation or sample frequencies makes a significant difference to how statistical tests are applied to substantive scientific models.

3. COMMON CAUSES, DISPOSITIONS, AND EXPLANATION

A good deal of attention has been paid in the recent philosophical literature to causally based explanations, especially those that make use of common causes to explain associations between observed events. An important consequence of the complexity of social phenomena is that we shall often have to be satisfied with partial explanations of the phenomena, a fact stressed by Cook and Campbell (see also Humphreys (1983)). A central issue here is whether such explanations should take place by means of test factors and partial correlations at the population level, or in terms of underlying latent variables construed in a dis positional sense. Much of the early work on probabilistic causality was of the first kind, largely because of the empiricist insistence on using only relative frequency accounts of probability. There has been a move away from this towards more realistically construed explanations in terms of causes, and there are valuable lessons about scientific realism and theoretical terms to be learned from consideration of latent variable and multiple indicator models. Although they do not stress this aspect of their work, part of Glymour and Scheines' strategy is a sophisticated version of a common cause explanatory strategy, where explanations of observed correlations (between indicator variables) are given in terms of unobservable theoretical common causes (latent variables). When considering the interpretation of latent variables, it is often

useful to construe them as causal dispositions to produce observable manifestations in terms of measureable indicator variables, and these dispositions can themselves often be seen as grounded in the structural



features of the system under investigation. (Blalock, for example notes that the definitions of many abstract concepts used in these models will involve causal features. For those unfamiliar with the basic concepts of dispositional ontologies, the first three sections of Fetzer's article provide a succinct development of those ideas.)

Such a dispositional approach is explicitly taken by Duncan. The particular device used by him is the simple but suggestive framework of Rasch models, and his treatment is important for a number of reasons. Rather than using correlations at the population level, and employing explanatory variables at that level, probabilistic dispositions possessed by individuals can serve as the basis for models which enable us, among other things, to distinguish between changes in social factors (which are of sociological interest) and changes in individual psychological dis positions (which are of interest to social psychology). The particular example which he treats in detail deals with relationships between attitudes, manifested usually by linguistic reports of intentions, and behaviours, or actions carrying through the verbally expressed in tentions. The model allows us to test the hypothesis that attitude and behaviour depend upon the same underlying disposition. Of special interest is his emphasis on inconsistent attitudes and behaviour, where expressed intent is followed by failure to act and vice versa. Precisely because expressed intent is a poor predictor in such cases, the difference between his model, which emphasizes the role of individual propensities, and traditional correlation approaches which emphasize the predictive relation between intent and action, can be clearly seen.

We thus have here a beautiful example of how explanation can take place without the concomitant need for a high degree of predictability.

A key feature of the argument involves checking the constancy of the propensity parameter across subpopulations stratified by means of variables causally relevant to the observed behaviour. Invariance of this propensity parameter characterizes it as social, rather than in dividual. If this kind of invariance argument is correct, it provides a powerful device for distinguishing causal influences operating at the social level from those operating at the individual level, as well as its primary use here in establishing the identity of the individual dis positions responsible for both attitude and behaviour manifestations. A further feature of his approach is that it shows how independence of outcomes at the individual level can produce artifactual associations at the population level by virtue of the inhomogeneity of the population


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with respect to the individual propensities. This possibility of spurious associations arising from aggregation processes can arise in a number of different ways (Simpson's paradox and ecological correlations are two well-known facets of this), and it illustrates the important difference between dealing with causal relations at the level of individual propen sities and dealing with them at the population level. Duncan's approach is robustly individualistic in character, with

different dispositions being attached to different members of the population. In general, however, individual dispositions will depend in part upon the causal influences operating in the environment within which they are located. This context-dependence of propensities is the key feature which Fetzer isolates (in the second half of his article) as having been ignored in the discussion about "frequency dependent causation". As he points out, not only is this terminology misleading, for this phenomenon does not constitute a distinctively different kind of causation, but by according due credit to the relational nature of many dispositions, one can see that the phenomena discussed in this area are simply special cases of a general property of propensities. (It is also clear that this context-sensitivity of causal dispositions is one reason why generalizability is such a problem). Furthermore, as he argues at the end of his article, once this fact that individuals are not causally closed systems has been recognized, the truth of methodological individualism becomes highly contingent. Without interaction between independent systems, the thesis will usually hold; with interactions it usually will not. The most striking example of this last point comes when the interacting systems are of exactly the same kind, yet dis tinctively different properties emerge from their causal interaction. If one approaches this issue from the standpoint of an atomistic, non causal, ontology, this phenomenon is essentially inexplicable.

4. PROBABILISTIC CAUSALITY AND CAUSAL MODELING

My opening assertion about the lack of contact between philosophical accounts of causation and scientific work is not entirely accurate, for there does exist a body of philosophical literature devoted to issues in probabilistic causality, much of it stemming from the framework constructed by Patrick Suppes (1970) in his monograph on the topic. In his article here, Suppes proves a theorem of considerable importance for the qualitative theory of probabilistic causality: a result stating the



conditions under which causal relations in nonMarkov models are transitive. This result generalizes an earlier result of Eells and Sober (1983), as well as providing a considerably simpler proof for the

Markov case. Although Suppes did provide links with quantitative theories of

probabilistic causality in his monograph, surprisingly little work has been done to follow up that connection, and the quantitative causal models look at first sight as if they have little to do with the qualitative accounts. This apparent difference is misleading, however, and it is worth indicating what the relationship is between the two. A formal connection can be gained in the following way. Define R(A, B) = [P(A/B)-P(A/- B)] to be the relevance

difference. Then an event or event type B is positively (negatively) relevant to an event or event type A just in case R(A, B)>0 (<0). Otherwise it is irrelevant. Let IA be the indicator variable for the event type A, and IB be the indicator variable for the event type B. Then, since E(IA) = P(A\ it follows that Cov(IA, IB) = [P(AB) - P(A)P(JB)]. Then, by simple manipulations, we have that R(A, B) =

[P(AB) - P(A)P(B)]/P(B)P(-B), and since Var(IB) = P(B)P(-B\ it follows that R(A, B) = Cov(JA, JB)/Var(JB) = bUI? the regression coefficient of IA on IB. Hence we have that IA = R(A, B)IB + + P(A/ ? B)+ U. Now suppose that IB changes from 0 to 1 (i.e., the situation changes from ? B to B because B occurs). Then there is a contribution of R(A, B) to JA which is added to the base level of P(A/ ? B) and which is due to the occurrence of B. This purely formal fact can be explained in terms of the effect of the change from ? B to B on the propensity of the system to produce A. The change deter ministically produces an increase in the propensity from its base level P(A/ - B) to a new value P(A/B). Assuming as we have done that this change in IB is the only change which occurs prior to the consequent change in JA, we can correctly assert that the increase in the propensity is caused by the change in IB, that is, the occurrence of B. Thus far, everything has been couched in terms of a recognizable

covariational account of causation. At this point, however, after the contribution of B has been taken into account through its increase in the propensity, it is a matter of sheer chance whether or not A occurs, i.e., whether JA takes on the value 1 or 0. U then represents the de facto contribution of chance to the eventual outcome, and the representation makes quite clear the peculiarity of the indeterministic


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case, because chance must "top up" the propensity value to unity, or "drain" it to zero. Events come in ontological chunks, and this all-or-nothing feature of the discrete case makes it appear as if chance cooperates with the causal factor to always ensure that exactly A or ?A occurs. Yet this is, I think, a rather misleading way of putting it, because once we have described the contribution of B to the occurrence of A, there is no further explanation to be had. After citing the causal factor (the change in IB) the event A either just happens or just does not happen. That is the nature of chance. There is one further result in the dichotomous case which is worth

noting. As we saw earlier, the function which minimizes the in deterministic element in the model is (for the dichotomous case considered here) f(IA) = E{IAIIB = b) = P(A/IB). In particular, because the regression function is the best predictor of IA, we have that when B occurs, the best predictor of whether A will occur is P(A/B), and when ?B occurs, the best predictor is P(A/-B). Thus the conditional probability alone is the best predictor in the dichotomous case, but it is the change in conditional probability R(A, B), and not the conditional probability itself which is explanatory. This distinction is disguised within a dichotomous event ontology by the difficulty in distinguishing the occurrence of B from the change from ? B to B, but it is clear in the variable-oriented framework discussed here. We thus

have here one simple example of how the change from a simple event ontology of causation to a more generalized one of causal relations between variables can be illuminating. Furthermore, if we are willing to allow that the apparatus of causal models can be applied to dummy variables such as the indicator functions of events, then there is a natural connection between the basic concepts of probabilistic causality and of causal models, in the sense that the former are simply a special case of the more general apparatus of the latter.

5. OUASI-EXPERIMENTATION

There are three distinctive modes of empirical investigation open to the social scientist interested in discovery of causal connections: classical laboratory experimentation, randomized experiments, and quasi experimentation. The first two should be familiar to most readers. A full treatment of the latter is available in Cook and Campbell (1979), and their article here on the topic is sufficiently clear and comprehensive



that I shall not elaborate much upon it. Perhaps the most valuable aspect of their methodology is the insistence on the need for an absolutely explicit consideration of threats to the validity of causal inferences. These threats come in four kinds: threats to internal validity (incorrect inferences about causal connections within the framework of the specific system under investigation), threats to external validity (nongeneralizability of results outside the specific context investigated), threats to statistical conclusion validity (where assumptions required for statistical methods have not been satisfied), and threats to construct validity (where mis-specification of the model or incorrect conceptual frameworks lead to invalid causal inferences).

There are some important differences between the quasi-experimen tal approach and the causal modeling techniques, however. For exam ple, whereas Blalock views causal claims as being about the causal models, Cook and Campbell want their methodology to give us answers about causal relationships within the social systems themselves. Again, Cook and Campbell insist that manipulability criteria for causal judgements lie at the heart of most claims in this area, and that the apparatus of causal modeling is best applicable in those cases where the situations are relatively static and such manipulability is not available to us. These are differences of real interest, and the recent revival of philosophical interest in the experimental method (see e.g., Bhaskar (1975) and Hacking (1983)) bodes well for a more realistic empiricism.

NOTE

* This paper forms the editorial introduction to a special issue of Synthese devoted to causation in the social sciences. I want to stress that the views and interpretations which follow are my own, and that where I have drawn connections between the work of different authors, it is not to be inferred that those authors would necessarily agree to the correctness of the connections I have made. Whenever mention is made of an author's

paper without a specific reference cited, that paper is the one included in this issue. This article was written while the author held NSF grant SES 84-10898, and the support is gratefully acknowledged, as is the hospitality of the Center for Philosophy of Science, University of Pittsburgh.

REFERENCES

Bhaskar, R.: 1975, A Realist Theory of Science, Humanities Press, Atlanta Highlands. Cook, T. and Campbell, D.: 1979, Quasi-Experimentation: Design and Analysis Issues

for Field Settings, Houghton Mifflin Company, Boston.


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Eells, E. and Sober, E.: 1983, 'Probabilistic Causality and the Question of Transitivity', Philosophy of Science 50, 35-57.

Hacking, I.: 1983, Representing and Intervening, Cambridge University Press, Cambridge.

Humphreys, P.: 1983, 'Aleatory Explanations Expanded', in P. Asquith and T. Nickles (eds.), PSA 1982, Volume 2, East Lansing, Philosophy of Science Association, pp. 208-223.

Humphreys, P.: 1986, 'Some Issues Regarding Structure in Social Science Models', in P. Asquith and P. Kitcher (eds.), PSA 1984, Volume 2, East Lansing, Philosophy of Science Association.

Suppes, P.: 1970, A Probabilistic Theory of Causality, North-Holland, Amsterdam.

Department of Philosophy University of Virginia 521 Cabell Hall Charlottesville, VA 22901 U.S.A.

EDITORS NOTE

The last paper, 'Preserving the Prisoner's Dilemma', was submitted independently and for that reason, is not covered in the preface.


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