4 papers
Learning Treatment Representations for Downstream Instrumental Variable Regression
Shiangyi Lin, Hui Lan, Vasilis Syrgkanis
Traditional instrumental variable (IV) estimators face a fundamental constraint: they can only accommodate as many endogenous treatment variables as available instruments. This lim…
A Meta-learner for Heterogeneous Effects in Difference-in-Differences
Hui Lan, Haoge Chang, Eleanor Dillon +1
We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel tr…
Regularized DeepIV with Model Selection
Zihao Li, Hui Lan, Vasilis Syrgkanis +2
In this paper, we study nonparametric estimation of instrumental variable (IV) regressions. While recent advancements in machine learning have introduced flexible methods for IV es…
Causal Q-Aggregation for CATE Model Selection
Hui Lan, Vasilis Syrgkanis
Accurate estimation of conditional average treatment effects (CATE) is at the core of personalized decision making. While there is a plethora of models for CATE estimation, model s…