Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
This work addresses a limitation in causal inference for practical applications where heterogeneous effects matter, though it appears incremental as it extends existing two-stage frameworks to heterogeneity.
The paper tackles the problem of estimating heterogeneous long-term causal effects, which is understudied compared to average effects, by proposing several two-stage nonparametric estimators including propensity-based, regression-based, and multiple robust variants. The results show effectiveness across semi-synthetic and real-world datasets, with theoretical analysis of asymptotic properties.
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.