Causal Reasoning

This is a commentary on Mathias Frisch's book Causal Reasoning in Physics (Cambridge 2014). This commentary was presented at the 2016 Pacific Division Meeting of the American Philosophical Association in a session sponsored by the Society for the Metaphysics of Science.

Tight correlations between mental states and brain states have been observed time and again within the ethology of biologically ingrained animal behaviors, the comparative psychology of animal minds, the evolutionary psychology of mental adaptations, the behavioral genetics of inherited mental traits, the developmental psychology of the maturing mind, the psychopharmacology of mind-altering substances, and cognitive neuroscience more generally. They imply that our mental lives are only made possible because of brain activity—that having a functioning brain is a necessary condition for (...) having conscious experiences. This mind-brain dependence thesis yields predictions that have been spectacularly confirmed by the evidence, whereas its antithesis has been massively disconfirmed—at least so long as it is not formulated so vaguely as to yield no specific empirical consequences at all. Dualists are thus forced to make a difficult choice: either retain a belief in personal survival at the expense of ignoring or dismissing the implications of our best evidence, or accept those implications at the expense of acknowledging that the prospects for personal survival are extremely dim. -/- 1. Introduction: A Probabilistic Argument -- 2. Scientific Reasoning I: Mill’s Methods and Modern Epidemiological Principles - 2.1 When Correlation Indicates Causation -- 3. Scientific Reasoning II: Inference to the Best Explanation, Testability, and Predictive Success - 3.1 Parsimony: Tempering Explanations to Minimize False Beliefs - 3.2 Plausibility: Harmonizing Explanations with Background Knowledge - 3.3 Testability and Confirmation: Checking Hypotheses Against the Data - 3.3.1 Confirmation of the Dependence Thesis from Failed Attempts to Falsify It - 3.3.2 Confirmation of the Dependence Thesis from the Falsification of its Rival - 3.4 Scope: Bringing Disparate Facts Together - 3.5 Fertility: Generating New Lines of Research - 3.6 Overview -- 4. Scientific Reasoning III: Bayesian and Information-Theoretic Approaches - 4.1 Bayesian Probability - 4.2 Bayesian Confirmation Theory - 4.3 A Bayesian Approach to the Dependence and Independence Theses - 4.4 The Probabilistic Price of Ad Hoc Accommodation: Simplicity vs. Goodness of Fit - 4.4.1 Simplicity Reflected in the Likelihood - 4.4.2 Simplicity Reflected in the Prior Probability - 4.5 Prediction vs. Accommodation - 4.6 Bayesian Analysis of the Dependence and Independence Theses -- 5. The Dualist’s Dilemma: Reject Science, or Reject Personal Survival? - 5.1 What’s Left for a Soul to Do? -- 6. Conclusion. (shrink)

Inductive reasoning is fundamental to human cognition, yet it remains unclear how we develop this ability and what might influence our inductive choices. We created novel categories in which crucial factors such as domain and category structure were manipulated orthogonally. We trained 403 4–9-year-old children to categorise well-matched natural kind and artefact stimuli with either featural or relational category structure, followed by induction tasks. This wide age range allowed for the first full exploration of the developmental trajectory of inductive reasoning (...) in both domains. We found a gradual transition from perceptual to categorical induction with age. This pattern was stable across domains, but interestingly, children showed a category bias one year later for relational categories. We hypothesise that the ability to use category information in inductive reasoning develops gradually, but is delayed when children need to process and apply more complex category structures. (shrink)

According to the transitive dynamics model, people can construct causal structures by linking together configurations of force. The predictions of the model were tested in two experiments in which participants generated new causal relationships by chaining together two (Experiment 1) or three (Experiment 2) causal relations. The predictions of the transitive dynamics model were compared against those of Goldvarg and Johnson-Laird’s model theory (Goldvarg & Johnson- Laird, 2001). The transitive dynamics model consistently predicted the overall causal relationship drawn by participants (...) for both types of causal chains, and, when compared to the model theory, provided a better fit to the data. The results suggest that certain kinds of causal reasoning may depend on force dynamic—rather than on logical or purely statistical—representations. (shrink)

We present a logic, \, for the discovery of deterministic causal regularities starting from empirical data. Our approach is inspired by Mackie’s theory of causes as INUS-conditions, and implements a more recent adjustment to Mackie’s theory according to which the left-hand side of causal regularities is required to be a minimal disjunction of minimal conjunctions. To derive such regularities from a given set of data, we make use of the adaptive logics framework. Our knowledge of deterministic causal regularities is, as (...) Mackie noted, most often gappy or elliptical. The adaptive logics framework is well-suited to explicate both the internal and the external dynamics of the discovery of such gappy regularities. After presenting \, we first discuss these forms of dynamics in more detail. Next, we consider some criticisms of the INUS-account and show how our approach avoids them, and we compare \ with the CNA algorithm that was recently proposed by Michael Baumgartner. (shrink)

Causal conditional reasoning means reasoning from a conditional statement that refers to causal content. We argue that data from causal conditional reasoning tasks tell us something not only about how people interpret conditionals, but also about how they interpret causal relations. In particular, three basic principles of people's causal understanding emerge from previous studies: the modal principle, the exhaustive principle, and the equivalence principle. Restricted to the four classic conditional inferences—Modus Ponens, Modus Tollens, Denial of the Antecedent, and Affirmation of (...) the Consequent—causal conditional reasoning data are only partially able to support these principles. We present three experiments that use concrete and abstract causal scenarios and combine inference tasks with a new type of task in which people reformulate a given causal situation. The results provide evidence for the proposed representational principles. Implications for theories of the na ve understanding of causality are discussed. (shrink)

Randomized controlled trials (RCTs) are widely taken as the gold standard for establishing causal conclusions. Ideally conducted they ensure that the treatment ‘causes’ the outcome—in the experiment. But where else? This is the venerable question of external validity. I point out that the question comes in two importantly different forms: Is the specific causal conclusion warranted by the experiment true in a target situation? What will be the result of implementing the treatment there? This paper explains how the probabilistic theory (...) of causality implies that RCTs can establish causal conclusions and thereby provides an account of what exactly that causal conclusion is. Clarifying the exact form of the conclusion shows just what is necessary for it to hold in a new setting and also how much more is needed to see what the actual outcome would be there were the treatment implemented. (shrink)

There is an ongoing debate in economics between the design-based approach and the structural approach. The main locus of contention regards how best to pursue the quest for credible causal inference. Each approach emphasizes one element ? sharp study designs versus structural models ? but these elements have well-known limitations. This paper investigates where a researcher might look for credibility when, for the causal question under study, these limitations are binding. It argues that seeking variety of evidence ? understood specifically (...) as using multiple means of determination to robustly estimate the same causal effect ? constitutes such an alternative and that applied economists actually take advantage of it. Evidential variety is especially relevant for a class of macro-level causal questions for which the design-based and the structural approaches appear to have limited reach. The use of evidential variety is illustrated by drawing on the literature on the institutional determinants of the aggregate unemployment rate. (shrink)

Newsome ((2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87-107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable (...) indicators of causation, and focus on different types of causation (type vs. token). There are certainly debates in the research field, but the theoretical landscape is not as fractured as Newsome suggests, and a potential unifying framework has already emerged using causal Bayes nets. Philosophical work on causal epistemology matters for psychologists, but not in the way Newsome suggests. (shrink)

We make constant use of causal beliefs in our everyday lives without giving much thought to the source of those beliefs, even for situations about which we have no specific prior causal knowledge. We can ask two distinct types of questions about these causal judgments: descriptive questions and normative questions . The primary goal of this dissertation is to apply normative research on causal judgment to our descriptive theories. ;I begin this dissertation by describing the primary results of research on (...) the normative question: the framework of Bayesian networks, and algorithms that provably extract maximal causal information from observations and interventions in the environment. I then show that we can use the framework of Bayesian networks to represent the three major descriptive theories of causal judgment. The three theories are not directly comparable, however, since one of them is algorithmic, while the other two are computational. Therefore, I provide a fully general characterization of the Rescorla-Wagner model. ;With this computational description of the Rescorla-Wagner model in hand, we can compare all three of the theories against the known experimental data. Unfortunately, the experimental data has all been obtained using one particular experimental methodology, which contains serious problems. In addition to critiquing the currently dominant methodology, I provide a "proof of concept" of a different methodology that does not face these problems. ;More importantly, the psychological theories all focus on parameter estimation in a fixed causal structure, rather than learning the actual structure. Therefore, I consider the experimental question: can people learn complex causal structures? The data from those experiments reveal that people appear to use a wide array of learning strategies. The epistemology of our causal beliefs is substantially more complex than has previously been thought. ;Finally, at the end of this dissertation, I step back and examine the implications of these experimental results for descriptive theories of causal judgment. I also point towards several open research questions, both descriptive and normative, raised by the work in this dissertation. (shrink)

One of the most central problems in scientific research is the search for explanations of some aspect of nature for which empirical data is available. One seeks to identify the causal processes explaining the data, in the form of a model of the aspect of nature under study. Although traditional statistical approaches are excellent for finding statistical dependencies in a body of empirical data, they prove inadequate at finding the causal structure in the data. New graphical algorithmic approaches have been (...) proposed to automatically discover the causal structure in the data. Based on strong connections between graph theoretic properties and statistical aspects of causal influences, fundamental assumptions about the data can be used to infer a graphical structure, which is used to construct models describing the exact causal relations in the data. If the data contain correlated errors, latent variables must be introduced to explain the causal structure in the data. There is usually a large set of equivalent causal models with latent variables, representing competing alternatives, which entail similar statistical dependency relations. ;The central problem in this dissertation is the study of the theoretical limits to reliable causal inference. Given a body of statistical distribution information on a finite set of variables, we seek to characterize the set of all causal models satisfying this distribution. Current approaches only characterize the set of models which satisfy limited properties of this distribution, notably its relations of probabilistic conditional independence. Such models are semi-Markov equivalent. Some of these models might however not satisfy other properties of the distribution, which cannot be expressed as simple conditional independence relations on marginal distributions. We seek to go beyond semi-Markov equivalence. To do so, we first formally characterize the variation in graphical structure within a semi-Markov equivalence class of models. We then determine possible consequences of this variation as either experimentally testable features of models, or as testable features of marginal distributions. (shrink)

In this paper I offer an anti-Humean critique to Williamson and Russo’s approach to medical mechanisms. I focus on one of the specific claims made by Williamson and Russo, namely the claim that micro-structural ‘mechanisms’ provide evidence for the stability across populations of causal relationships ascertained at the (macro-) level of (test) populations. This claim is grounded in the epistemic account of causality developed by Williamson, an account which—while not relying exclusively on mechanistic evidence for justifying causal judgements—appeals nevertheless to (...) mechanisms, and rejects their anti-Humean interpretation in terms of capacities, powers, potencies, etc. By using (and expanding on) Cartwright’s basic critique against Humean mechanisms, I suggest that, in order to move beyond the level of plausibility, Williamson and Russo’s position is in need of a clarification as to the occurent reading of the components, functioning and interferences of mechanisms. Relatedly, as concerns Williamson’s epistemic account of causation, I argue that this account is in need of a more straightforward answer as to what truth-makers its causal claims should have. (shrink)

An interventionist account of causation characterizes causal relations in terms of changes resulting from particular interventions. I provide a new example of a causal relation for which there does not exist an intervention satisfying the common interventionist standard. I consider adaptations that would save this standard and describe their implications for an interventionist account of causation. No adaptation preserves all the aspects that make the interventionist account appealing. Part of the fallout is a clearer account of the difficulties in characterizing (...) so-called “soft” interventions. (shrink)

This survey presents some of the main principles involved in discovering causal relations. They belong to a large array of possible assumptions and conditions about causal relations, whose various combinations limit the possibilities of acquiring causal knowledge in different ways. How much and in what detail the causal structure can be discovered from what kinds of data depends on the particular set of assumptions one is able to make. The assumptions considered here provide a starting point to explore further the (...) foundations of causal discovery procedures, and how they can be improved. (shrink)

Two kinds of causal inference rules which are widely used by social scientists are investigated. Two conceptions of causation also widely used are explicated — the INUS and probabilistic conceptions of causation. It is shown that the causal inference rules which link correlation, a kind of partial correlation, and a conception of causation areinvalid. It is concluded anew methodology is required for causal inference.

Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly (...) through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science. (shrink)

According to a view widely held among philosophers of science, the notion of cause has no legitimate role to play in mature theories of physics. In this paper I investigate the role of what physicists themselves identify as causal principles in the derivation of dispersion relations. I argue that this case study constitutes a counterexample to the popular view and that causal principles can function as genuine factual constraints. IntroductionCausality and Dispersion RelationsNorton's SkepticismConclusion.

We have examined only a few of the basic questions about causal inference that result from Reichenbach's two principles. We have not considered what happens when the probability distribution is a mixture of distributions from different causal structures, or how unmeasured common causes can be detected, or what inferences can reliably be drawn about causal relations among unmeasured variables, or the exact advantages that experimental control offers. A good deal is known about these questions, and there is a good deal (...) more to find out. (shrink)

I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...) incorrectly let large causes ‘overshadow’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations. (shrink)

Clark Glymour, Richard Scheines, Peter Spirtes and Kevin Kelly. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling.

Word count (excluding abstract and references): 2,498 words. Address for correspondence: T. Kushnir, Psychology Department, University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650. Phone: 510-205-9847. Fax: 510-642- 5293. E-mail: [email protected].

Introduction -- Simple linear causality : one thing makes another happen -- The cognitive science of simple causality : why do we get stuck? -- Domino causality : effects that become causes -- Cyclic causality : loops and feedback -- Spiraling causality : escalation and de-escalation -- Mutual causality : symbiosis and bi-directionality -- Relational causality : balances and differentials -- Across time and distance : detecting delayed and distant effects -- "What happened?" vs. "what's going on?" : thinking about (...) steady states -- What you can't see does matter : attending to obvious and non-obvious causes -- It's not always a case of who did it : minding passive and unintentional causality -- Step by step, or not : the mind-bending concept of simultaneous causality -- Figuring out what to count on : dealing with stochastic causality -- Isn't anybody in charge around here? : attending to distributed causality and emergence -- Summing up : the implications for helping a new generation understand causal complexity -- Putting it all together : teaching for causal complexity. (shrink)

Special issue. With contributions by Malcolm Forster, Rocio Garcia-Rotamero and Ulrich Hoffrage, Christian Jakob, Kevin Korb and Erik Nyberg, Michael Smithson, Daniel Steel, Brad Weslake, and Jon Williamson.

Causal Bayes nets have been developed in philosophy, statistics, and computer sciences to provide a formalism to represent causal structures, to induce causal structure from data and to derive predictions. Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning and they were used as formal models of mental causal models. A crucial assumption made by them is the Markov condition, which informally states that variables are (...) independent of other variables that are not their direct or indirect effects conditional on their immediate causes. Whether people’s inferences conform to the causal Markov and the faithfulness condition has recently been investigated empirically. A review of respective research indicates that inferences frequently violate these conditions. This finding challenges some uses of causal Bayes nets in psychology. They entail that causal Bayes nets may not be appropriate to derive predictions for causal model theories of causal reasoning. They also question whether causal Bayes nets as a rational model are empirically descriptive. They do not challenge, however, causal Bayes nets as normative models and their usage as formal models of causal reasoning. (shrink)

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data (...) relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. (shrink)

The term “historical epistemology” can be read in two different ways: (1) as referring to a program of ‘historicizing’ epistemology, in the sense of a critique of traditional epistemology’s tendency to gloss over historical context, or (2) as a manifesto of ‘epistemologizing’ history, i.e. as a critique of radical historicist and relativist approaches. In this paper I will defend a position in this second sense. I show that one can account for the historical development and diversity of science without disavowing (...) the relevance of a (normatively understood) epistemology and without denying the existence of human cognitive universals across historical and cultural differences. In support of my thesis, I draw on cognitive scientific research on causal and symbolic cognition, arguing that causal understanding constitutes a basic part of science, which, in the course of its development, becomes more and more superimposed by a culturally and historically variable symbolic superstructure. (shrink)

The discounting principle states that 'the role of a given cause in producing a given effect is discounted if other plausible causes are present' (Kelley, 1972, p. 8). The principle has only been tested with cases where the two explanations are of the same kind (i.e., causal explanations). However, explanations of properties of objects, people, or events often involve functions. Zebras have stripes in order to be better camouflaged. Humans have eyebrows to keep sweat from running into their eyes. Adrenaline (...) is secreted in order to modulate fight and flight responses. Thus, what happens when we are faced with two different kinds of explanation for the same property: one functional and one causal? People evaluated explanations of properties for natural kinds and artefacts. Functional explanations were discounted in favour of causal explanations, however this was only true for properties of artefacts. The presence of an alternative explanation for properties of natural kinds did not affect the plausibility of either kind of explanation. (shrink)

The main focus of this paper is the question as to what it is for an individual to think of her environment in terms of a concept of causation, or causal concepts, in contrast to some more primitive ways in which an individual might pick out or register what are in fact causal phenomena. I show how versions of this question arise in the context of two strands of work on causation, represented by Elizabeth Anscombe and Christopher Hitchcock, respectively. I (...) then describe a central type of reasoning that, I suggest, a subject has to be able to engage in, if we are to credit her with causal concepts. I also point out that this type of reasoning turns on the idea of a physical connection between cause and effect, as articulated in recent singularist approaches of causation. (shrink)

Contemporary philosophical debates about causation are dominated by two approaches, which are often referred to as difference-making and causal process approaches to causation, respectively. I provide a characterization of the dialectic between these two approaches, on which that dialectic turns crucially on the question as to whether our common sense concept of causation involves a commitment to locality – i.e., to the claim that causal relations are always subject to spatial constraints. I then argue that we can extract from existing (...) work on perceptual judgement (specifically work that invokes the notion of a simple theory of perception) materials for an argument in favour of a positive answer to that question, and that this work can also help to bring out a distinctive kind of role that a commitment to locality plays in our reasoning about causal relationships. (shrink)