David Bohm's interpretation of quantum mechanics yields a quantum potential, Q. In his early work, the effects of Q are understood in causal terms as acting through a real (quantum) field which pushes particles around. In his later work (with Basil Hiley), the causal understanding of Q appears to have been abandoned. The purpose of this paper is to understand how the use of certain metaphors leads Bohm away from a causal treatment of Q, and to evaluate (...) the use of those metaphors. (shrink)

In this article, we examine the empirical association between corporate governance (CG) and corporate social responsibility (CSR) engagement by investigating their causal effects. Employing a large and extensive US sample, we first find that while the lag of CSR does not affect CG variables, the lag of CG variables positively affects firms’ CSR engagement, after controlling for various firm characteristics. In addition, to examine the relative importance of stakeholder theory and agency theory regarding the associations among CSR, CG, and (...) corporate financial performance (CFP), we also examine the relation between CSR and CFP. After correcting for endogeneity bias, our results show that CSR engagement positively influences CFP, supporting the conflict-resolution hypothesis based on stakeholder theory, but not the CSR overinvestment argument based on agency theory. Furthermore, firms’ CSR engagement with the community, environment, diversity, and employees plays a significantly positive role in enhancing CFP. (shrink)

Prior researchers have studied individual components of a theoretical decision-making model. This paper presents the results of a more complete study of the model components and presents limited support of theory. The study examines the relative importance of regulatory, organizational, and personal constructs on an individual''s ethical sensitivity. Auditors from the major international accounting firms, located in two southeastern cities, are surveyed. Structural equation modeling is used to allow for the simultaneous evaluation of the three constructs of interest. The results (...) indicate that the regulatory and organizational constructs are negatively correlated with the personal experience construct. The three constructs are not significant causal factors on ethical sensitivity. This result may be due to the manner in which ethical sensitivity is typically measured or may indicate that the complexity of the ethical decision-making process is not fully captured in the theoretical models. Thus, the models suggested in the prior literature and the results presented in prior studies of the individual components may need to be reconsidered. (shrink)

Rubin and Pearl offered approaches to causal effect estimation and Lewis and Pearl offered theories of counterfactual conditionals. Arguments offered by Pearl and his collaborators support a weak form of equivalence such that notation from the rival theory can be re-purposed to express Pearl’s theory in a way that is equivalent to Pearl’s theory expressed in its native notation. Nonetheless, the many fundamental differences between the theories rule out any stronger form of equivalence. A renewed emphasis on comparative (...) research can help to guide applications, further develop each theory, and better understand their relative strengths and weaknesses. (shrink)

I consider recent strategies proposed by econometricians for extrapolating causal effects from experimental to target populations. I argue that these strategies fall prey to the extrapolator’s circle: they require so much knowledge about the target population that the causal effects to be extrapolated can be identified from information about the target alone. I then consider comparative process tracing as a potential remedy. Although specifically designed to evade the extrapolator’s circle, I argue that CPT is unlikely to facilitate extrapolation (...) in typical econometrics and evidence-based policy applications. To argue this, I offer a distinction between two kinds of extrapolation, attributive and predictive, the latter being prevalent in econometrics and evidence-based policy. I argue that CPT is not helpful for predictive extrapolation when using the kinds of evidence that econometricians and evidence-based policy researchers prefer. I suggest that econometricians may need to consider qualitative evidence to overcome this problem. (shrink)

The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal (...) effects within Pearl’s do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders. (shrink)

Randomized controlled trials are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population. Having at hand a sample of the target population of interest allows us to generalize the causal effect. Identifying the treatment effect in the target population requires covariates to capture all treatment effect modifiers that are shifted between the two sets. Standard (...) estimators then use either weighting, outcome modeling, or combine the two in doubly robust approaches. However, such covariates are often not available in both sets. In this article, after proving L 1 {L}^{1} -consistency of these three estimators, we compute the expected bias induced by a missing covariate, assuming a Gaussian distribution, a continuous outcome, and a semi-parametric model. Under this setting, we perform a sensitivity analysis for each missing covariate pattern and compute the sign of the expected bias. We also show that there is no gain in linearly imputing a partially unobserved covariate. Finally, we study the substitution of a missing covariate by a proxy. We illustrate all these results on simulations, as well as semi-synthetic benchmarks using data from the Tennessee student/teacher achievement ratio, and a real-world example from critical care medicine. (shrink)

Social constructionism about race is a common view, but there remain questions about what exactly constitutes constructed race. Some hold that our concepts and conceptual practices construct race, and some hold that the causal consequences of these concepts and conceptual practices also play a role. But there is a third option, which is that the causal effects of our concepts and conceptual practices constitute race, but not the concepts and conceptual practices themselves. This paper reconsiders an argument for (...) the reality of race that grows out of the role of racial kinds in social scientific generalizations. It then uses recent work on the correlation of racial attitudes with behaviors to raise questions about the sufficiency, and perhaps also the necessity, of our concepts and conceptual practices in constituting constructed race, thus understood. (shrink)

The mainstream view today in neuroscience, biology, psychology, and philosophy, is that your conscious will has no effect upon your bodily actions beyond what is already caused by purely mechanical processes acting alone. Thus you are claimed to be, in essence, a mechanical automaton, with perhaps some elements of pure chance thrown in. Your natural belief that your willful efforts can have physical effects is called, accordingly, “The Illusion of Conscious Will”.

Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed to quantify deviation from conditional exchangeability, given measured confounders. These sensitivity parameters are combined with the observed data to produce a “bias-corrected” estimate of the causal effect of interest. We provide important generalizations of these sensitivity analyses, by allowing for arbitrary (...) exposures and a wide range of different causal effect measures, through the specification of the target causal effect as a parameter in a generalized linear model with the arbitrary link function. We show how our generalized sensitivity analysis can be easily implemented with standard software, and how its sensitivity parameters can be calibrated against measured confounders. We demonstrate our sensitivity analysis with an application to publicly available data from a cohort study of behavior patterns and coronary heart disease. (shrink)

Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precisions. To investigate the extent of this variability, we consider the simplest non-trivial non-linear (...) model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading-order asymptotics, we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result that is not captured by the recent leading-order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size and cannot rely on purely graphical criteria. (shrink)

Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute (...) an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study. (shrink)

Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. (...) To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival. (shrink)

Randomized controlled trials sometimes test interventions that aim to improve existing services targeted to a subset of individuals identified after randomization. Accordingly, the treatment could affect the composition of service recipients and the offered services. With such bias, intention-to-treat estimates using data on service recipients and nonrecipients may be difficult to interpret. This article develops causal estimands and inverse probability weighting estimators for complier populations in these settings, using a generalized estimating equation approach that adjusts the standard errors for (...) estimation error in the IPW weights. While our focus is on more general clustered RCTs, the methods also apply to nonclustered RCTs. Simulations show that the estimators achieve nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from a large-scale RCT testing the effects of early childhood services on children’s cognitive development scores. An R program for estimation is available for download. (shrink)

Analysts often use data-driven approaches to supplement their knowledge when selecting covariates for effect estimation. Multiple variable selection procedures for causal effect estimation have been devised in recent years, but additional developments are still required to adequately address the needs of analysts. We propose a generalized Bayesian causal effect estimation algorithm to perform variable selection and produce double robust estimates of causal effects for binary or continuous exposures and outcomes. GBCEE employs a prior distribution (...) that targets the selection of true confounders and predictors of the outcome for the unbiased estimation of causal effects with reduced standard errors. The Bayesian machinery allows GBCEE to directly produce inferences for its estimate. In simulations, GBCEE was observed to perform similarly or to outperform DR alternatives. Its ability to directly produce inferences is also an important advantage from a computational perspective. The method is finally illustrated for the estimation of the effect of meeting physical activity recommendations on the risk of hip or upper-leg fractures among older women in the study of osteoporotic fractures. The 95% confidence interval produced by GBCEE is 61% narrower than that of a DR estimator adjusting for all potential confounders in this illustration. (shrink)

Spirtes, Glymour and Scheines [Causation, Prediction, and Search Springer] described a pointwise consistent estimator of the Markov equivalence class of any causal structure that can be represented by a directed acyclic graph for any parametric family with a uniformly consistent test of conditional independence, under the Causal Markov and Causal Faithfulness assumptions. Robins et al. [Biometrika 90 491–515], however, proved that there are no uniformly consistent estimators of Markov equivalence classes of causal structures under those assumptions. (...) Subsequently, Kalisch and B¨uhlmann [J. Mach. Learn. Res. 8 613–636] described a uniformly consistent estimator of the Markov equivalence class of a linear Gaussian causal structure under the Causal Markov and Strong Causal Faithfulness assumptions. However, the Strong Faithfulness assumption may be false with high probability in many domains. We describe a uniformly consistent estimator of both the Markov equivalence class of a linear Gaussian causal structure and the identifiable structural coefficients in the Markov equivalence class under the Causal Markov assumption and the considerably weaker k-Triangle-Faithfulness assumption. (shrink)

Causal inference in the empiricalsciences is based on counterfactuals. The mostcommon approach utilizes a statistical model ofpotential outcomes to estimate causal effectsof treatments. On the other hand, one leadingapproach to the study of causation inphilosophical logic has been the analysis ofcausation in terms of counterfactualconditionals. This paper discusses and connectsboth approaches to counterfactual causationfrom philosophy and statistics. Specifically, Ipresent the counterfactual account of causationin terms of Lewis's possible-world semantics,and reformulate the statistical potentialoutcome framework using counterfactualconditionals. This procedure highlights (...) variousproperties and mechanisms of the statisticalmodel. (shrink)

Causal Dispositionalism provides an account of causation based on an ontology of causal powers, properties with causal essence. According to the account, causation can be analysed in terms of the interaction of powers and its subsequent production of their effect. Recently, Baltimore, J. A. has raised a challenge against two competing approaches, the compositional view and the mutual manifestation view, to explain what makes powers interactive – the interaction gap. In this paper, we raise the challenge (...) of explaining what makes powers productive – the production gap. While Baltimore’s verdict is tentatively favouring, we find both approaches wanting. Our conclusion is that Causal Dispositionalists should take Baltimore’s and our critique seriously. Powers cannot cause their effects just by bearing the name “causal”. To deserve their names, more metaphysical details are needed. (shrink)

Despite the voluminous literature on biological functions produced over the last 40 years, few philosophers have studied the concept of function as it is used in neuroscience. Recently, Craver (forthcoming; also see Craver 2001) defended the causal role theory against the selected effects theory as the most appropriate theory of function for neuroscience. The following argues that though neuroscientists do study causal role functions, the scope of that theory is not as universal as claimed. Despite the strong prima (...) facie superiority of the causal role theory, the selected effects theory (when properly developed) can handle many cases from neuroscience with equal facility. It argues this by presenting a new theory of function that generalizes the notion of a ‘selection process’ to include processes such as neural selection, antibody selection, and some forms of learning—that is, to include structures that have been differentially retained as well as those that have been differentially reproduced. This view, called the generalized selected effects theory of function, will be defended from criticism and distinguished from similar views in the literature. (shrink)

Despite recent efforts to improve on counterfactual theories of causation, failures to explain how effects depend on their causes are still manifest in a variety of cases. In particular, theories that do a decent job explaining cases of causal preemption have problems accounting for cases of causal intransitivity. Moreover, the increasing complexity of the counterfactual accounts makes it difficult to see why the concept of causation would be such a central part of our cognition. In this paper, I (...) propose an account of our causal thinking that not only explains the hitherto puzzling variety of causal judgments, but also makes it intelligible why we would employ such an elusive concept. (shrink)

La autora presenta algunas criticas generales al proyecto de reducir las leyes causales a probabilidades. Además, muestra que las leyes causales son imprescindibles para poder diferenciar las strategias efectivas de las que no lo son y da un criterio para considerar cuando podemos deducir causalidad a través de datos estadísticos.

The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject. The first half goes through the concepts of causality, with very little in the way of estimation. It introduces the concept of identification thoroughly and clearly and discusses it as a process of trying to isolate variation that has a (...) class='Hi'>causal interpretation. Subjects include heavy emphasis on data-generating processes and causal diagrams. Concepts are demonstrated with a heavy emphasis on graphical intuition and the question of what we do to data. When we "add a control variable" what does that actually do? (shrink)

The past few years have seen several novel information-theoretic measures of causal emergence developed within the scientific community. In this paper I will introduce one such measure, called ‘effective information’, and describe how it is used to argue for causal emergence. In brief, the idea is that certain kinds of complex system are structured such that an intervention characterised at the macro-level will be more informative than one characterised at the micro-level, and that this constitutes a form of (...) causal emergence. Having introduced this proposal, I will then assess the extent to which it is genuinely ‘causal’ and/or ‘emergent’, and argue that it supports only an epistemic form of causal emergence that is not as exciting as it first seems. (shrink)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production (IEP). I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: (...) regularities under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This article provides a systematic explanation of such assumptions. We define five potential outcome types whose means are involved in various effect definitions. We tackle their mean/distribution’s identification, starting with the one that requires the weakest assumptions and gradually building up to the one that requires the strongest assumptions. This presentation shows clearly why an assumption is required for one estimand (...) and not another, and provides a succinct table from which an applied researcher could pick out the assumptions required for identifying the causal effects they target. Using a running example, the article illustrates the assembling and consideration of identifying assumptions for a range of causal contrasts. For several that are commonly encountered in the literature, this exercise clarifies that identification requires weaker assumptions than those often stated in the literature. This attention to the details also draws attention to the differences in the positivity assumption for different estimands, with practical implications. Clarity on the identifying assumptions of these various estimands will help researchers conduct appropriate mediation analyses and interpret the results with appropriate caution given the plausibility of the assumptions. (shrink)

We argue that not all cases of before-effect involve causation and ask how to demarcate cases of before-effect in which the events that follow exert causal influence over the before-effect from cases in which they do not.

The significance of counterfactual thinking in the causal judgement process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgement. Three experiments examined this comparison by manipulating the task frame used to focus participants' thinking about a target event. Prior to making judgements about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what (...) the actor could have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgement. Implications for research on causal judgement and mental representation are discussed. (shrink)

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using (...) tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. (shrink)

We investigated how people design interventions to affect the outcomes of causal systems. We propose that the abstract structural properties of a causal system, in addition to people's content and mechanism knowledge, influence decisions about how to intervene. In Experiment 1, participants preferred to intervene at specific locations in a causal chain regardless of which content variables occupied those positions. In Experiment 2, participants were more likely to intervene on root causes versus immediate causes when they were (...) presented with a long-term goal versus a short-term goal. These results show that the structural properties of a causal system can guide the design of interventions. (shrink)

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production. I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: regularities (...) under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems. (shrink)

To justify inductive inference and vanquish classical skepticisms about human memory, external world realism, etc., Richard Fumerton proposes his “inferential internalism,” an epistemology whereby humans ‘see’ by Russellian acquaintance Keynesian probable relations (PRs) between propositions. PRs are a priori necessary relations of logical probability, akin to but not reducible to logical entailments, such that perceiving a PR between one’s evidence E and proposition P of unknown truth value justifies rational belief in P to an objective degree. A recent critic of (...) inferential internalism is Alan Rhoda, who questions its psychological plausibility. Rhoda argues that in order to see necessary relations between propositions E and P, one would need acquaintance with too many propositions at once, since our evidence E is often complex. In this paper, I criticize Rhoda’s implausibility objection as too quick. Referencing the causal status effect (CSE) from psychology, I argue that some of the complex features of evidence E contribute to our type-categorizing it as E-type, and thus we do not need to ‘see’ all of the complex features when we see the PR between E and P. My argument leaves unchanged Fumerton’s justificatory role for the PR, but enhances its psychological plausibility. (shrink)

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (...) (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, we argue that an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of our extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one. Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure. We discuss these results in relation to work on counterfactual thinking, causal modeling, and late-preemption. (shrink)

People tend to judge more recent events, relative to earlier ones, as the cause of some particular outcome. For instance, people are more inclined to judge that the last basket, rather than the first, caused the team to win the basketball game. This recency effect, however, reverses in cases of overdetermination: people judge that earlier events, rather than more recent ones, caused the outcome when the event is individually sufficient but not individually necessary for the outcome. In five experiments (...) (N = 5507), we find evidence for the recency effect and the primacy effect for causal judgment. Traditionally, these effects have been a problem for counterfactual views of causal judgment. However, an extension of a recent counterfactual model of causal judgment explains both the recency and the primacy effect. In line with the predictions of the extended counterfactual model, we also find that, regardless of causal structure, people tend to imagine the counterfactual alternative to the more recent event rather than to the earlier one (Experiment 2). Moreover, manipulating this tendency affects causal judgments in the ways predicted by this extended model: asking participants to imagine the counterfactual alternative to the earlier event weakens (and sometimes eliminates) the interaction between recency and causal structure, and asking participants to imagine the counterfactual alternative to the more recent event strengthens the interaction between recency and causal structure (Experiments 3 & 5). We discuss these results in relation to work on counterfactual thinking and causal modeling. (shrink)

Hermeneutics should take Gadamer’s claims about experience and reality seriously, the hermeneutic urgency, described in the following concise ways: aus der Wahrheit des Erinnerns etwas entgegensetzen: das immer noch und immer wieder Wirkliche. WuM, p. XXVIeine Erfahrung, die Wirklichkeit erfährt und selber wirklich ist. WuM, p. 329.Die Erfahrung lehrt, Wirkliches anzuerkennen. WuM, p. 339.preceded by a general remark about the aim of historical knowledge:eine Erkenntnis, die versteht, daß etwas so ist, weil sie versteht, daß es so gekommen ist. WuM, p. (...) 2This indicates an ontological primacy of history, which is a singular processual event with a certain narrative dimension which can also be called history. Through this essential and at the same time particular finitude, meaning is given to us – and all the general aspects of explained reality are possible only inside such an effective history which unites event and narration. The article will elaborate the special character of historical effects compared to the effects in the modern scheme of cause and effect and suggest a way of reading the classical distinction between understanding and explanation in a way which differs somewhat from Gadamer’s, but also from Georg Henrik von Wright’s similar concerns. It is also argued that Dilthey’s notion of lived experience remains relevant inside a very much needed hermeneutic effort to include nature in history. (shrink)

The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by (...) asking whether or not the machine would have gone off in the absence of I of 2 objects that had been placed on it as a pair. Cue competition effects were demonstrated only in 5- to 6-year-olds using this mode of assessing causal reasoning. (shrink)