Counterfactual Generative Models for Time-Varying Treatments

Shenghao Wu, Wenbin Zhou, Minshuo Chen, Shixiang Zhu

Primary Area: causal reasoning

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Keywords: causal inference, policy evaluation, longitudinal causal inference, time series, generative models

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TL;DR: We propose a flexible generative framework for high-d counterfactual outcome generation under time-varying treatment.

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 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.

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Submission Number: 6428