Behavioural Causal Inference.
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 a...
| Publicado en: | Review of Economic Studies Vol. 93; no. 2; pp. 1323 - 1354 |
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| Autor principal: | |
| Formato: | Artículo |
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Oxford University Press / USA
Mar2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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. |
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