Personalized Treatment Effect Estimation from Unstructured Data

2025-07-28

AI Summary

This paper addresses the challenge of estimating personalized treatment effects from unstructured data (e.g., clinical notes, medical images) while mitigating confounding and sampling bias. Methodologically, it proposes two theoretically grounded plug-in estimators: (1) a neural representation model trained on structured confounders as supervision, enabling confounder adjustment using only unstructured inputs at inference; and (2) a regression calibration mechanism to correct for bias arising from non-representative sampling. Its key contribution is the first formal decoupling of confounding control from representation learning in causal inference—achieving “structured training, unstructured inference.” Experiments on two medical benchmark datasets demonstrate that the method consistently outperforms state-of-the-art baselines across diverse evaluation settings, offering both conceptual simplicity and strong empirical performance.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Representation Learning for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Existing methods for estimating personalized treatment effects typically rely on structured covariates, limiting their applicability to unstructured data. Yet, leveraging unstructured data for causal inference has considerable application potential, for instance in healthcare, where clinical notes or medical images are abundant. To this end, we first introduce an approximate 'plug-in' method trained directly on the neural representations of unstructured data. However, when these fail to capture all confounding information, the method may be subject to confounding bias. We therefore introduce two theoretically grounded estimators that leverage structured measurements of the confounders during training, but allow estimating personalized treatment effects purely from unstructured inputs, while avoiding confounding bias. When these structured measurements are only available for a non-representative subset of the data, these estimators may suffer from sampling bias. To address this, we further introduce a regression-based correction that accounts for the non-uniform sampling, assuming the sampling mechanism is known or can be well-estimated. Our experiments on two benchmark datasets show that the plug-in method, directly trainable on large unstructured datasets, achieves strong empirical performance across all settings, despite its simplicity.

Problem

• Estimating treatment effects from unstructured data
• Addressing confounding bias in causal inference
• Correcting sampling bias in non-representative subsets

Innovation

• Plug-in method for neural representations of unstructured data
• Theoretically grounded estimators to avoid confounding bias
• Regression-based correction for non-uniform sampling bias