5 papers · 1 filter
Potential weights and implicit causal designs in linear regression
Jiafeng Chen
Applied researchers routinely use linear regression to estimate causal effects, justified by quasi-experimental treatment variation, while leaving assumptions on treatment assignme…
Empirical Bayes shrinkage (mostly) does not correct the measurement error in regression
Jiafeng Chen, Jiaying Gu, Soonwoo Kwon
In the value-added literature, it is often claimed that regressing on empirical Bayes shrinkage estimates corrects for the measurement error problem in linear regression. We clarif…
Empirical Bayes When Estimation Precision Predicts Parameters
Jiafeng Chen
Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the esti…
Nonparametric Treatment Effect Identification in School Choice
Jiafeng Chen
This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment algorithms, students face both lot…
Reinterpreting demand estimation
Jiafeng Chen
This paper clarifies how and why structural demand models (Berry and Haile, 2014, 2024) predict unit-level counterfactual outcomes. We do so by casting structural assumptions equiv…