High-dimensional Inference for Dynamic Treatment Effects

TL;DR

This paper introduces a new doubly robust representation for estimating dynamic treatment effects in high-dimensional settings, improving robustness and consistency over traditional methods.

Contribution

The paper proposes a novel DR representation for intermediate outcomes that enhances robustness and achieves consistency with high-dimensional confounders.

Findings

New DR representation offers superior robustness guarantees.

Method achieves consistency with high-dimensional confounders.

Validated through simulations and real data application.

Abstract

Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermediate conditional outcome models that leads to superior robustness guarantees. The proposed method achieves consistency even with high-dimensional confounders, as long as at least one nuisance function is appropriately parametrized for each exposure time and treatment path. Our results represent a significant step forward as they provide new robustness guarantees. The key to achieving these results is our new DR representation, which offers superior inferential performance while requiring weaker assumptions. Lastly, we confirm our findings in practice through simulations and a real data application.