Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference

2025-10-21

AI Summary

Medical causal inference is hindered by the scarcity of real-world clinical data, while existing synthetic data generation methods fail to preserve—specifically for treatment effect estimation—the essential characteristics of covariate distributions, treatment assignment mechanisms, and outcome generation mechanisms. To address this, we propose STEAM, the first framework that systematically models the triple generative mechanism underlying therapeutic data and introduces a dedicated evaluation metric suite tailored for causal inference tasks. STEAM integrates generative modeling with structural causal models, explicitly optimizing fidelity in both treatment and outcome mechanisms. Extensive experiments demonstrate that STEAM significantly outperforms state-of-the-art baselines across multiple causal evaluation metrics—particularly under challenging settings involving high-dimensional covariates, nonlinear relationships, and strong confounding dependencies.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This makes synthetic data a potentially valuable asset that enables these medical analyses, along with the development of new inference methods themselves. Generative models can produce synthetic data that closely approximate real data distributions, yet existing methods do not consider the unique challenges that downstream causal inference tasks, and specifically those focused on treatments, pose. We establish a set of desiderata that synthetic data containing treatments should satisfy to maximise downstream utility: preservation of (i) the covariate distribution, (ii) the treatment assignment mechanism, and (iii) the outcome generation mechanism. Based on these desiderata, we propose a set of evaluation metrics to assess such synthetic data. Finally, we present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine that mimics the data-generating process of data containing treatments and optimises for our desiderata. We empirically demonstrate that STEAM achieves state-of-the-art performance across our metrics as compared to existing generative models, particularly as the complexity of the true data-generating process increases.

Problem

• Improving synthetic data generation for medical causal inference
• Evaluating synthetic data quality for treatment effect analysis
• Addressing unique challenges in treatment-focused causal inference tasks

Innovation

• Generates synthetic data for medical treatment effect analysis
• Optimizes data to preserve treatment and outcome mechanisms
• Introduces evaluation metrics for synthetic data utility