  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.
