  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.
