Counterfactual Generative Models for Time-Varying Treatments
This addresses a critical problem in public health and clinical science for decision-makers dealing with sequential treatments, though it appears to be an incremental improvement over existing generative models.
The paper tackles the challenge of estimating counterfactual outcomes for time-varying treatments in high-dimensional settings, where conventional methods fail to capture individual disparities. The proposed conditional generative framework generates high-quality counterfactual samples and outperforms state-of-the-art baselines.
Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability re-weighting, and supports integration with state-of-the-art conditional generative models such as the guided diffusion and conditional variational autoencoder. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines.