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Subjective Causality in Choice

Andrew Ellis () and Heidi Christina Thysen

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Abstract: An agent makes a stochastic choice from a set of lotteries. She infers the outcomes of each available lottery using a subjective causal model represented by a directed acyclic graph, and consequently may misinterpret correlation as causation. Her choices affect her inferences, which in turn affect her choices, so the two together must form a personal equilibrium. We show how an analyst can identify the agent's subjective causal model from her random choice rule. Her choices reveal the chains of causal reasoning that she undergoes, and these chains pin down her subjective causal model. In addition, we provide necessary and sufficient conditions that allow an analyst to test whether the agent's behavior is compatible with the model.

Date: 2021-06, Revised 2021-10
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