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Identifying Causal Effects in Information Provision Experiments

Dylan Balla-Elliott

The Review of Economics and Statistics2026https://doi.org/10.1162/rest.a.1745article
AJG 4ABDC A*
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0.50

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 e!ects of beliefs on outcomes. Standard estimators therefore understate these causal e!ects. I propose an alternative local least squares (LLS) estimator that recovers a representative unweighted average e!ect in a broad class of learning rate models that generalize Bayesian updating. In five of six recent studies, estimates of the e!ects of beliefs on outcomes increase. In two, they more than double.

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https://doi.org/https://doi.org/10.1162/rest.a.1745

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@article{dylan2026,
  title        = {{Identifying Causal Effects in Information Provision Experiments}},
  author       = {Dylan Balla-Elliott},
  journal      = {The Review of Economics and Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1162/rest.a.1745},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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