Counterfactual Generative Models for Time-Varying Treatments

• URL: http://arxiv.org/abs/2305.15742v5
• Date: Sat, 13 Jul 2024 08:03:27 GMT
• Title: Counterfactual Generative Models for Time-Varying Treatments
• Authors: Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu,
• Abstract summary: Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science. We propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment. We present a thorough evaluation of our method using both synthetic and real-world data.
• Score: 15.208067770012283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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