Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908252654010368 |
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| author | Chen, Weilin Cai, Ruichu Wan, Junjie Yang, Zeqin Hernández-Lobato, José Miguel |
| author_facet | Chen, Weilin Cai, Ruichu Wan, Junjie Yang, Zeqin Hernández-Lobato, José Miguel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination Chen, Weilin Cai, Ruichu Wan, Junjie Yang, Zeqin Hernández-Lobato, José Miguel Machine Learning 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. |
| title | Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.18960 |