提出非参数方法，精准估算长期因果效应的个体差异。
Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
- 分两阶段构建无参数估计器，融合短期实验与长期观测数据。
- 理论分析显示其渐近性质稳定，适用条件明确。
- 适用于医疗、政策评估等需个性化决策的领域。
长期因果推断在多个科学领域日益受到关注。现有方法主要通过结合长期观测数据与短期实验数据来估计平均长期因果效应，但对异质性长期因果效应的稳健有效估计仍研究不足，严重制约了实际应用。本文提出几种两阶段非参数估计器，包括基于倾向得分、回归和多重稳健的估计方法，并在较弱假设下对其渐近性质进行了全面理论分析，旨在厘清各类估计器在何种条件下表现更优。在多个半合成及真实世界数据集上的大量实验验证了理论结果，表明所提方法具有有效性。
原文摘要 · Abstract (English)
Long-term causal inference has drawn increasing attention in many scientific domains. Existing methods mainly focus on estimating average long-term causal effects by combining long-term observational data and short-term experimental data. However, it is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects, significantly limiting practical applications. In this paper, we propose several two-stage style nonparametric estimators for heterogeneous long-term causal effect estimation, including propensity-based, regression-based, and multiple robust estimators. We conduct a comprehensive theoretical analysis of their asymptotic properties under mild assumptions, with the ultimate goal of building a better understanding of the conditions under which some estimators can be expected to perform better. Extensive experiments across several semi-synthetic and real-world datasets validate the theoretical results and demonstrate the effectiveness of the proposed estimators.
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