A Quantitative Causal Model Theory of Conditional Reasoning

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Abstract

The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability of MP is a judgment of causal power, the probability that the antecedent cause is efficacious in bringing about the consequent effect. Acceptability of AC is a judgment of diagnostic strength, the probability of the antecedent cause given the consequent effect. The model proposes that acceptability judgments are derived from a causal Bayesian network with a common effect structure in which the probability of the consequent effect is a function of the antecedent cause, alternative causes, and disabling conditions. In 2 experiments, the model was tested by collecting judgments of the causal parameters of conditionals and using them to derive predictions for MP and AC acceptability using 0 free parameters. To assess the validity of the model, its predictions were fit to the acceptability ratings and compared to the fits of 3 versions of Mental Models Theory. The fits of the causal model theory were superior. Experiment 3 provides direct evidence that people engage in a causal analysis and not a direct calculation of conditional probability when assessing causal conditionals. The causal model theory represents a synthesis across the disparate literatures on deductive, probabilistic, and causal reasoning.

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