@inproceedings{
wu2023counterfactual,
title={Counterfactual Generative Models for Time-Varying Treatments},
author={Shenghao Wu and Wenbin Zhou and Minshuo Chen and Shixiang Zhu},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=NQEhg8WdvG}
}
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 weighting. 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.
Counterfactual Generative Models for Time-Varying Treatments
Shenghao Wu Wenbin Zhou Minshuo Chen Shixiang Zhu