Behavioural Causal Inference
研究了决策者从观察数据中推断因果效应时，因控制变量选择不当导致的错误，并通过均衡分析得出错误控制带来的福利成本上限。
Abstract 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.