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Behavioural Causal Inference
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AI
DOI：10.1093/restud/rdaf050.png)
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Abstract
En 中文
When inferring causal effects from correlational data, a common practice by professional researchers but also lay people is to control for potential confounders. Inappropriate controls produce erroneous causal inferences. I model decision-makers (DMs) who use endogenous observational data to learn actions' causal effect on payoff-relevant outcomes. Different DM types use different controls. Their resulting choices affect the very correlations they learn from, thus calling for an equilibrium analysis of the steady-state welfare cost of bad controls. I obtain tight upper bounds on this cost. Equilibrium forces drastically reduce it when types' sets of controls contain one another.
Keywords:
Non-rational expectations
Causal inference
Bad controls
Bayesian networks
Misspecified models
Worst-case analysis