Interpreting TSLS Estimators in Information Provision Experiments

Vod Vilfort and Whitney Zhang

American Economic Review: Insights, 2025, vol. 7, issue 3, 376-95

Abstract: In information provision experiments, researchers often estimate the causal effects of beliefs on actions using two-stage least squares (TSLS). This paper formalizes exclusion and monotonicity conditions that ensure that TSLS recovers a positive-weighted average of causal effects. We assess common TSLS estimators for both passive and active control designs from the literature; we find that two commonly used passive control estimators generally allow for negative weights. The choice of passive control estimator affects the magnitude and significance of estimates in simulations and in an empirical application. We give practical recommendations for addressing these issues.

JEL-codes: C26 C90 D21 D83 E23 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aea:aerins:v:7:y:2025:i:3:p:376-95

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DOI: 10.1257/aeri.20240353

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