There has been little research on infants’ development of causal inference in the second year after birth. We report an experiment in which 9to 18-month-old infants viewed visual sequences consisting of three looming shapes, one after another. Half of the sequences (causes) were predictive of an attention-getting reward (effect), and the other half were nonpredictive. The statistical complexity...

Learning a causal effect from observational data requires strong assumptions. One possibility is to use instrumental variables, which are typically justified by background knowledge. It is possible, under further assumptions, to discover whether a variable is structurally instrumental to a target causal effect X → Y . However, the few existing approaches are lacking on how general these assumpt...

Previous work showed that people‟s causal judgments are modeled better as estimates of the probability that a causal relationship exists (a qualitative inference) than as estimates of the strength of that relationship (a quantitative inference). Here, using a novel task, we present experimental evidence in support of the importance of qualitative causal inference. Our findings cannot be explain...

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In two recent communications, Cole and Frangakis and VanderWeele conclude that the consistency rule used in causal inference is an assumption that precludes any side-effects of treatment/exposure on the outcomes of interest. They further develop auxiliary notation to make this assumption formal and explicit. I argue that the consistency rule is a theorem in the logic of counterfactuals and need...

This paper explores the relationship between a manipulability conception of causation and the causal Markov condition (CM). We argue that violations of CM also violate widely shared expectations—implicit in the manipulability conception—having to do with the absence of spontaneous correlations. They also violate expectations concerning the connection between independence or dependence relations...

One of the goals of the social sciences is to explain social phenomena. In this explanatory enterprise, causal relations, established by means of the so-called ‘causal models’, play a major role. This paper advances the view that causal models, by modelling causal mechanisms, (ought to) provide an explanation of social phenomena and should be seen as a model of explanation.1

In this paper, we present a new approach to support the decision of selecting one object out of a set of alternatives. As compared to previous approaches, the distinctive feature of our approach is that neither the user, nor the system need to build a model of user’s preferences. Our proposal is to integrate a system for interactive data exploration and analysis with a multimedia explanation fa...

The causal notions embodied in the concept of Granger causality have been argued to belong to a different category than those of Judea Pearl’s Causal Model, and so far their relation has remained obscure. Here, we demonstrate that these concepts are in fact closely linked by showing how each relates to straightforward notions of direct causality embodied in settable systems, an extension and re...