Counterfactual Generative Models for Time-Varying Treatments

TL;DR

This paper introduces a novel conditional generative framework for estimating high-dimensional, time-varying treatment counterfactuals, addressing distribution mismatch and outperforming existing methods in synthetic and real-world data.

Contribution

The proposed method is a new generative approach that models time-varying treatments without explicit density estimation, improving counterfactual sample quality.

Findings

Generates high-quality counterfactual samples.

Outperforms state-of-the-art baselines.

Effective on synthetic and real-world data.

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.