Identifying Causal Effects in Information Provision Experiments
标准估计方法在信息提供实验中会低估信念对结果的因果效应，因为权重偏向更新幅度大但因果效应弱的个体。本文提出局部最小二乘估计量，能恢复无偏的平均效应，并在多项研究中使估计值显著增大。
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.