A causal model for causal priority

Recent attempts to fix the direction of causal priority without reference to the direction of temporal priority have begun with an analysis of the causal relation itself. I offer a method, based on causal modelling theory, designed to determine the direction of causal priority while remaining as agnostic as possible about the nature of the causal relation.     

A test of the joint model of causal attribution

The joint model integrates Mill's methods of difference and agreement for making causal attributions, and introduces a novel type of context attributions which reflect the opposite categories of the standard attribution responses provided in previous covariation research external versus the person, general versus the stimulus, and stable versus the occasion). The joint model predicts that attributions to standard causes require Mill's method of difference and that attributions to context causes require Mill's method of agreement. Two empirical studies demonstrated that the joint model fitted adequately with all of subject's standard and context attribution responses, in contrast to earlier theorizing and data involving only the method of difference (cf. Cheng & Novick, 1990) or only the method of agreement (cf. Hilton, Smith & Kim, 1995) which received less empirical support. © 1997 John Wiley & Sons, Ltd.     

A test of causal sequences in two models for development of career-orientation in women

To evaluate the tenability of two causal orderings of variables in the development of career-orientation, responses of 92 college senior women to Need Achievement, Dating Frequency, and Career-Orientation items were analyzed. The postulated orderings were derived from two models: a compensatory model implying that failure in a woman's heterosexual affiliation leads to high nAch which, in turn, produces high C-O; an enrichment model suggesting that a woman's high nAch may lead to high C-O which, in turn, decreases heterosexual affiliation. Criteria for temporal sequences as per the Simon-Blalock correlational procedures rendered the enrichment model more tenable.     

A causal argument for dualism

Dualism holds (roughly) that some mental events are fundamental and non-physical. I develop a prima facie plausible causal argument for dualism. The argument has several significant implications. First, it constitutes a new way of arguing for dualism. Second, it provides dualists with a parity response to causal arguments for physicalism. Third, it transforms the dialectical role of epiphenomenalism. Fourth, it refutes the view that causal considerations prima facie support physicalism but not dualism. After developing the causal argument for dualism and drawing out these implications, I subject the argument to a battery of objections. Some prompt revisions to the argument. Others reveal limitations in scope. It falls out of the discussion that the causal argument for dualism is best used against physicalism as a keystone in a divide and conquer strategy.     

Choosing optimal causal backgrounds for causal discovery

In two experiments, we studied the strategies that people use to discover causal relationships. According to inferential approaches to causal discovery, if people attempt to discover the power of a cause, then they should naturally select the most informative and unambiguous context. For generative causes this would be a context with a low base rate of effects generated by other causes and for preventive causes a context with a high base rate. In the following experiments, we used probabilistic and/or deterministic target causes and contexts. In each experiment, participants observed several contexts in which the effect occurred with different probabilities. After this training, the participants were presented with different target causes whose causal status was unknown. In order to discover the influence of each cause, participants were allowed, on each trial, to choose the context in which the cause would be tested. As expected by inferential theories, the participants preferred to test generative causes in low base rate contexts and preventative causes in high base rate contexts. The participants, however, persisted in choosing the less informative contexts on a substantial minority of trials long after they had discovered the power of the cause. We discuss the matching law from operant conditioning as an alternative explanation of the findings.     

The response styles theory of depression: A test of specificity and causal mediation

This prospective study tested the diathesis-stress and causal mediation components of the response styles theory of depression. In addition, it examined whether rumination predicts increases in anxious as well as depressive symptoms. At Time 1, 87 college students completed measures of rumination, hopelessness, depressive symptoms, and anxious symptoms. Participants also completed measures of hopelessness, depressive symptoms, and anxious symptoms at three time points later in the semester: immediately after receiving their most difficult midterm exam grade (Time 2), 4–8 hours later (Time 3), and 4 days later (Time 4). Regardless of exam outcome, the tendency to ruminate in response to depressed mood was associated with: (1) increases in anxious symptoms between Time 1 and Time 3; and (2) increases in both anxious and depressive symptoms between Time 1 and Time 4. In addition, the relationship between rumination and increases in both depressive and anxious symptoms was mediated by hopelessness. In other words, individuals with a ruminative response style exhibited increases in both depressive and anxious symptoms because they exhibited increases in hopelessness.     

A priori causal laws

Sober and Elgin defend the claim that there are a priori causal laws in biology. Lange and Rosenberg take issue with this on Humean grounds, among others. I will argue that Sober and Elgin don’t go far enough – there are a priori causal laws in many sciences. Furthermore, I will argue that this thesis is compatible with a Humean metaphysics and an empiricist epistemology.     

An empirical test of the covariation analysis in causal attribution

The present investigation was designed to reveal the cognitive inference processes associated with both detection and utilization of covariation information in causal attribution. Male undergraduates were (a) informed that a test was easy or difficult and shown a videotape in which (b) the test-taker's performance was high or low, and (c) covariation between the test-taker's effort expenditure and trial-by-trial outcome was present or absent. High performance was attributed to the test-taker's effort and ability, whereas low performance was attributed to the difficulty of the test. However, recognition of the covariance relationship decreased the attribution of high performance to ability and of low performance to test difficulty and increased the attribution of low performance to effort. Effort attribution in the high performance condition was independent of covariation information. The results are discussed in terms of the relationship between covariation information and typical beliefs about the causes of achievement outcomes.     

Impressions of force in visual perception of collision events: A test of the causal asymmetry hypothesis

When two objects interact they exert equal and opposite forces on each other. According to the causal asymmetry hypothesis, however, when one object has been identified as causal and the other as that in which the effect occurs, the causal object is perceived as exerting greater force on the effect object than the latter is perceived as exerting on the former. An example of this is a stimulus in which one object moves toward another stationary one, and when contact occurs the former stops and the latter moves away. In this situation the initially moving object is identified as causal, so the causal asymmetry hypothesis predicts that more force will be judged to be exerted by the moving object on the stationary one than by the stationary one on the moving one. Participants’ judgments consistently supported this hypothesis for a variety of stimuli in which kinematic parameters were varied, even when the initially moving object reversed direction after contact.     

Using the Rasch model to quantify the causal effect of test instructions

This experimental study was designed to quantify, by means of the Rasch model (RM), the effects of three instruction/scoring conditions on student measures and on the reliability of an achievement multiple-choice test in a field context. Examinees performed the test in one of three conditions which differed only in the instructions provided. Predictions regarding performance indicators were fulfilled, and the expected differences in reliability favoring omission-inducing instructions did appear. This difference in reliability was found for both Rasch and raw data and thus it can be concluded that the fact that results from previous studies failed to corroborate this prediction must have been due to the lack of important consequences of test scores for the students. The RM has served to neatly quantify the differences between instructions promoting guessing and instructions promoting omission under uncertainty, showing that the recommendation to omit is not only educationally but also psychometrically sound.     

A theory of causal learning in children: causal maps and Bayes nets

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.     

From covariation to causation: a test of the assumption of causal power

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shanks's critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shanks's and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction.     

Mood and personality: A search for the causal relationship1

Previous findings of correlations between mood and personality variables were noted. It was suggested that the establishment of a causal relationship was crucially important, not merely to the understanding of how mood is related to personality but to the conceptualization of mood as a source of error in personality measurement as well. A prior attempt to establish a causal role for mood failed, but may have done so for methodological reasons. The present study investigated the influence of a mood manipulation on personality measures. There was also an opportunity to replicate prior correlational findings independent of the effect of the mood manipulation. College students were asked to think of happy, sad, or affectively neutral events and then filled out several personality measures. A manipulation check confirmed that different moods were induced by this procedure. The prior correlations between mood and personality variables were replicated, but the mood manipulation did not affect any of the personality measures. The implications of these results for personality measurement and for the relation between mood and behavior were discussed.     

A causal ontology of objects,causal relations,and various kinds of action

Synthese - The explanatory/pragmatic-trial distinction enjoys a burgeoning philosophical and medical literature and a significant contingent of support among philosophers and healthcare...     

A generalized probabilistic theory of causal relevance

I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9.I would like to thank Nuel Belnap, John Earman, Richard Gale, Paul Humphreys, Satish Iyengar, Wes Salmon, and two anonymous referees for comments and discussion. I am also indebted to the members of an audience at the Center for Philosophy of Science at the University of Pittsburgh, where some of the ideas contained in this paper were presented.