Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination

• URL: http://arxiv.org/abs/2502.18960v1
• Date: Wed, 26 Feb 2025 09:17:04 GMT
• Title: Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
• Authors: Weilin Chen, Ruichu Cai, Junjie Wan, Zeqin Yang, José Miguel Hernández-Lobato,
• Abstract summary: Long-term causal inference has drawn increasing attention in many scientific domains.<n>It is still understudied how to robustly and effectively estimate heterogeneous long-term causal effects.<n>We propose several two-stage style non-parametric estimators for heterogeneous long-term causal effect estimation.
• Score: 37.491679058742925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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