DeepBlip: Estimating Conditional Average Treatment Effects Over Time

Understand

Structural nested mean models (SNMMs) are a principled approach to estimate the treatment effects over time.

• A particular strength of SNMMs is to break the joint effect of treatment sequences over time into localized, time-specific ``blip effects''.
• This decomposition promotes interpretability through the incremental effects and enables the efficient offline evaluation of optimal treatment policies without re-computation.
• However, neural frameworks for SNMMs are lacking, as their inherently sequential g-estimation scheme prevents end-to-end, gradient-based training.