AmirEmad Ghassami, Chang Liu, Alan Yang, David Richardson, Ilya Shpitser, Eric Tchetgen Tchetgen
arXiv 26 Jan 2022 · Statistics — Methodology · 7 citations (OpenAlex)
arXiv:2201.10743 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey, S., Chetty, R., and Imbens, G (2020) Combining experimental and observational data to estimate treatment effects on long term outcomes | 0.969 | 11 | 5 | 91% |
| 2 | Miao, W., Geng, Z., and Tchetgen Tchetgen, E. J (2018) Identifying causal effects with proxy variables of an unmeasured confounder | 0.843 | 4 | 4 | 75% |
| 3 | Sofer, T., Richardson, D. B., Colicino, E., Schwartz, J., and Tchetg… (2016) On negative outcome control of unobserved confounding as a generalization of difference-in-differences self | 0.843 | 4 | 3 | 75% |
| 4 | Athey, S. and Imbens, G. W (2006) Identification and inference in nonlinear difference-in-differences models | 0.737 | 3 | 3 | 67% |
| 5 | Cui, Y., Pu, H., Shi, X., Miao, W., and Tchetgen Tchetgen, E (2020) Semiparametric proximal causal inference | 0.737 | 3 | 3 | 67% |
| 6 | Tchetgen Tchetgen, E. J., Ying, A., Cui, Y., Shi, X., and Miao, W (2020) An introduction to proximal causal learning | 0.737 | 3 | 3 | 67% |
| 7 | Richardson, D. B. and Tchetgen Tchetgen, E. J (2022) Bespoke instruments: A new tool for addressing unmeasured confounders self | 0.737 | 3 | 2 | 100% |
| 8 | Achilles, C., Bain, H. P., Bellott, F., Boyd-Zaharias, J., Finn, J.,… (2008) Tennessee's Student Teacher Achievement Ratio (STAR) project | 0.644 | 2 | 2 | 100% |
| 9 | Angrist, J. D. and Pischke, J.-S (2008) Mostly harmless econometrics | 0.644 | 2 | 2 | 100% |
| 10 | Card, D (1990) The impact of the mariel boatlift on the miami labor market | 0.644 | 2 | 2 | 100% |
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