  Estimating dynamic treatment effects is essential across various disciplines,
offering nuanced insights into the time-dependent causal impact of
interventions. However, this estimation presents challenges due to the "curse
of dimensionality" and time-varying confounding, which can lead to biased
estimates. Additionally, correctly specifying the growing number of treatment
assignments and outcome models with multiple exposures seems overly complex.
Given these challenges, the concept of double robustness, where model
misspecification is permitted, is extremely valuable, yet unachieved in
practical applications. This paper introduces a new approach by proposing
novel, robust estimators for both treatment assignments and outcome models. We
present a "sequential model double robust" solution, demonstrating that double
robustness over multiple time points can be achieved when each time exposure is
doubly robust. This approach improves the robustness and reliability of dynamic
treatment effects estimation, addressing a significant gap in this field.
