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Causal Exaggeration: Unconfounded but Inflated Causal Estimates

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  • Vincent Bagilet

    (ENS de Lyon - École normale supérieure de Lyon - Université de Lyon)

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.

Suggested Citation

  • Vincent Bagilet, 2025. "Causal Exaggeration: Unconfounded but Inflated Causal Estimates," Working Papers hal-05426371, HAL.
    • Handle: RePEc:hal:wpaper:hal-05426371
    Note: View the original document on HAL open archive server: https://hal.science/hal-05426371v1
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