Causal Exaggeration: Unconfounded but Inflated Causal Estimates

Vincent Bagilet ()
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Vincent Bagilet: ENS de Lyon - École normale supérieure de Lyon - Université de Lyon

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Abstract: The credibility revolution has made causal inference methods ubiquitous in economics, yet it coexists with selection on significance. I show that these two phenomena interact in ways that reduce the reliability of published estimates: while causal identification strategies alleviate bias from confounders, they reduce statistical power and can generate another type of bias-exaggeration-when combined with selection on significance. I characterize this confounding-exaggeration trade-off theoretically and via realistic Monte Carlo simulations, and document its prevalence in the literature. I then propose practical solutions, including a tool to identify the variation actually driving identification.

Keywords: Causal Inference; Exaggeration; Statistical Power; Simulations; Applied Microeconomics; Package (search for similar items in EconPapers)
Date: 2025-12-19
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