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stat.ML2025
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…
stat.ML2025
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…