Causal Bayes nets as psychological theories of causal Reasoning

evidence from psychological research

York Hagmayer

pp. 1107-1126

in: Alexander Gebharter, Gerhard Schurz (eds), Causation, probability, and truth, Synthese 193 (4), 2016.

Abstract

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 (Glymour and Cooper, in Computation, causation, and discovery, 1999; Spirtes et al., in Causation, prediction, and search, 2000). Causal Bayes nets have been used as psychological theories in at least two ways. They were used as rational, computational models of causal reasoning (e.g., Gopnik et al., in Psychol Rev 111:3–32, 2004) and they were used as formal models of mental causal models (e.g., Sloman, in Causal models: how we think about the world and its alternatives, 2005). 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.