Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation
This work addresses sample selection bias in treatment effect estimation for high-dimensional data, offering a more robust estimator, though it is incremental as it builds on existing weighting schemes.
The paper tackles the challenge of high-dimensional heterogeneous treatment effect estimation by addressing the numerical instability of inverse probability weighting (IPW) methods, which cause estimation bias. The proposed differentiable Pareto-smoothed weighting framework corrects extreme weight values, outperforming existing methods in experiments.
There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but are predictive of potential outcomes. To avoid losing such predictive feature information, existing methods learn separate feature representations using inverse probability weighting (IPW). However, due to their numerically unstable IPW weights, these methods suffer from estimation bias under a finite sample setup. To develop a numerically robust estimator by weighted representation learning, we propose a differentiable Pareto-smoothed weighting framework that replaces extreme weight values in an end-to-end fashion. Our experimental results show that by effectively correcting the weight values, our proposed method outperforms the existing ones, including traditional weighting schemes. Our code is available at https://github.com/ychika/DPSW.