Identifying Causal Effects in Information Provision Experiments
Econometrics
2026-01-13 v5
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.
Cite
@article{arxiv.2309.11387,
title = {Identifying Causal Effects in Information Provision Experiments},
author = {Dylan Balla-Elliott},
journal= {arXiv preprint arXiv:2309.11387},
year = {2026}
}