Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination

TL;DR

This paper introduces nonparametric two-stage estimators for robustly estimating heterogeneous long-term causal effects by combining observational and experimental data, with theoretical analysis and empirical validation.

Contribution

It proposes novel nonparametric estimators for heterogeneous long-term causal effects and provides a comprehensive theoretical analysis of their properties under mild assumptions.

Findings

Proposed estimators outperform existing methods in various datasets.

Theoretical analysis confirms estimator consistency and robustness.

Empirical results validate the effectiveness of the proposed methods.

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.