Identifying Causal Effects in Information Provision Experiments

Dylan Balla-Elliott

Papers from arXiv.org

Abstract: Standard estimators in information provision experiments place more weight on individuals who update their beliefs more in response to new information. This paper shows that, in practice, these individuals who update the most have the weakest causal effects of beliefs on outcomes. Standard estimators therefore understate these causal effects. I propose an alternative local least squares (LLS) estimator that recovers a representative unweighted average effect in a broad class of learning rate models that generalize Bayesian updating. I reanalyze six published studies. In five, estimates of the causal effects of beliefs on outcomes increase; in two, they more than double.

Date: 2023-09, Revised 2026-01
New Economics Papers: this item is included in nep-ecm and nep-exp
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