Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866908562212519936 |
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| author | Ghassami, AmirEmad Liu, Chang Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen |
| author_facet | Ghassami, AmirEmad Liu, Chang Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen |
| contents | We study identifying and estimating the causal effect of a treatment variable on a long-term outcome using data from an observational and an experimental domain. The observational data are subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period; thus, long-term effects are unobserved, though short-term effects are available. Consequently, neither data source alone suffices for causal inference on the long-term outcome, necessitating a principled fusion of the two. We propose three approaches for data fusion for the purpose of identifying and estimating the causal effect. The first assumes equal confounding bias for short-term and long-term outcomes. The second weakens this assumption by leveraging an observed confounder for which the short-term and long-term potential outcomes share the same partial additive association with this confounder. The third approach employs proxy variables of the latent confounder of the treatment-outcome relationship, extending the proximal causal inference framework to the data fusion setting. For each approach, we develop influence function-based estimators and analyze their robustness properties. We illustrate our methods by estimating the effect of class size on 8th-grade SAT scores using data from the Project STAR experiment combined with observational data from the Early Childhood Longitudinal Study. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_10743 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects Ghassami, AmirEmad Liu, Chang Yang, Alan Richardson, David Shpitser, Ilya Tchetgen, Eric Tchetgen Methodology Econometrics Statistics Theory Machine Learning We study identifying and estimating the causal effect of a treatment variable on a long-term outcome using data from an observational and an experimental domain. The observational data are subject to unobserved confounding. Furthermore, subjects in the experiment are only followed for a short period; thus, long-term effects are unobserved, though short-term effects are available. Consequently, neither data source alone suffices for causal inference on the long-term outcome, necessitating a principled fusion of the two. We propose three approaches for data fusion for the purpose of identifying and estimating the causal effect. The first assumes equal confounding bias for short-term and long-term outcomes. The second weakens this assumption by leveraging an observed confounder for which the short-term and long-term potential outcomes share the same partial additive association with this confounder. The third approach employs proxy variables of the latent confounder of the treatment-outcome relationship, extending the proximal causal inference framework to the data fusion setting. For each approach, we develop influence function-based estimators and analyze their robustness properties. We illustrate our methods by estimating the effect of class size on 8th-grade SAT scores using data from the Project STAR experiment combined with observational data from the Early Childhood Longitudinal Study. |
| title | Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects |
| topic | Methodology Econometrics Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2201.10743 |