Counterfactual Generative Models for Time-Varying Treatments

Estimating average causal effects is a common practice to test new treatments. However, the average effect ''masks'' important individual characteristics in the counterfactual distribution, which may lead to safety, fairness, and ethical concerns. This issue is exacerbated in the temporal setting, where the treatment is sequential and time-varying, leading to an intricate influence on the counterfactual distribution. In this paper, we propose a novel conditional generative modeling approach to capture the whole counterfactual distribution, allowing efficient inference on certain statistics of the counterfactual distribution. This makes the proposed approach particularly suitable for healthcare and public policy making. Our generative modeling approach carefully tackles the distribution mismatch in the observed data and the targeted counterfactual distribution via a marginal structural model. Our method outperforms state-of-the-art baselines on both synthetic and real data.