High-dimensional Inference for Dynamic Treatment Effects
This work addresses a key challenge in causal inference for researchers and practitioners dealing with complex, high-dimensional data, representing an incremental improvement over existing doubly robust methods.
The paper tackles the problem of estimating dynamic treatment effects with high-dimensional confounders by proposing a novel doubly robust representation for intermediate conditional outcome models, achieving consistency under weaker assumptions and demonstrating superior robustness guarantees in simulations and real data.
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.