用可信赖的统计方法，为治疗效果估计提供精准不确定性量化。
A Systematic Review of Conformal Inference Procedures for Treatment Effect Estimation: Methods and Challenges
- 基于置信预测框架，无需强假设即可给出有限样本覆盖保证。
- 筛选11篇核心论文，系统梳理当前最先进的治疗效果估计方法。
- 适合关注医疗、政策决策中模型可靠性与风险控制的研究者。
治疗效应估计在医疗、经济和公共政策等领域的决策中至关重要。尽管灵活的机器学习模型已被广泛用于估计异质性治疗效应，但其点预测的内在不确定性仍难以量化。近年来，置信预测方法的进展解决了这一问题，可在无需昂贵计算、适应分布变化的前提下，对任意点预测模型提供频率学意义下的有限样本覆盖保证，且假设条件极少。该方法在高风险场景中具有显著应用潜力。本文对治疗效应估计中的置信预测方法进行系统综述，提供必要的理论背景。通过系统的筛选流程，我们选取并分析了11篇关键文献，识别并描述了该领域的最新研究进展。基于分析结果，我们提出了未来研究的方向。
原文摘要 · Abstract (English)
Treatment effect estimation is essential for informed decision-making in many fields such as healthcare, economics, and public policy. While flexible machine learning models have been widely applied for estimating heterogeneous treatment effects, quantifying the inherent uncertainty of their point predictions remains an issue. Recent advancements in conformal prediction address this limitation by allowing for inexpensive computation, as well as distribution shifts, while still providing frequentist, finite-sample coverage guarantees under minimal assumptions for any point-predictor model. This advancement holds significant potential for improving decision-making in especially high-stakes environments. In this work, we perform a systematic review regarding conformal prediction methods for treatment effect estimation and provide for both the necessary theoretical background. Through a systematic filtering process, we select and analyze eleven key papers, identifying and describing current state-of-the-art methods in this area. Based on our findings, we propose directions for future research.
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