Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang
arXiv 15 Feb 2022 · Statistics — Methodology · publishedJournal of the Royal Statistical Society Series B (Statistical Methodology) (2024) · 10 citations (OpenAlex)
arXiv:2202.07234 · PDF · DOI · OpenAlex · Extracted main text
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data. However, both types of data include observations of some short-term outcomes. In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i.e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature. To address this challenge, we exploit the sequential structure of multiple short-term outcomes, and develop three novel identification strategies for the average long-term treatment effect. We further propose three corresponding estimators and prove their asymptotic consistency and asymptotic normality. We finally apply our methods to estimate the effect of a job training program on long-term employment using semi-synthetic data. We numerically show that our proposals outperform existing methods that fail to handle persistent confounders.
appendix boundary found by appendix_command · 38% of the source is main text. Read the extracted text to check this.
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 | AmirEmad Ghassami, Ilya Shpitser, and Eric Tchetgen Tchetgen (2022) Combining experimental and observational data for identification of long-term causal effects | 0.935 | 11 | 4 | 82% |
| 2 | Yifan Cui, Hongming Pu, Xu Shi, Wang Miao, and Eric Tchetgen Tchetgen (2020) Semiparametric proximal causal inference | 0.928 | 4 | 4 | 100% |
| 3 | Wang Miao and Eric Tchetgen Tchetgen (2018) A confounding bridge approach for double negative control inference on causal effects (supplement and sample codes are included) | 0.928 | 4 | 4 | 100% |
| 4 | Guido Imbens, Nathan Kallus, and Xiaojie Mao (2021) Controlling for unmeasured confounding in panel data using minimal bridge functions: From two-way fixed effects to factor models self | 0.928 | 4 | 3 | 100% |
| 5 | Susan Athey, Raj Chetty, Guido W Imbens, and Hyunseung Kang (2019) The surrogate index: Combining short-term proxies to estimate long-term treatment effects more rapidly and precisely self | 0.916 | 13 | 5 | 77% |
| 6 | Susan Athey, Raj Chetty, and Guido Imbens (2020) Combining experimental and observational data to estimate treatment effects on long term outcomes, 2020 self | 0.909 | 28 | 8 | 75% |
| 7 | Nathan Kallus, Xiaojie Mao, and Masatoshi Uehara (2021) Causal inference under unmeasured confounding with negative controls: A minimax learning approach, 2021 self | 0.894 | 7 | 6 | 71% |
| 8 | Wang Miao, Zhi Geng, and Eric Tchetgen (2016) Identifying causal effects with proxy variables of an unmeasured confounder | 0.874 | 6 | 4 | 67% |
| 9 | Nishanth Dikkala, Greg Lewis, Lester Mackey, and Vasilis Syrgkanis (2020) Minimax estimation of conditional moment models | 0.811 | 4 | 2 | 100% |
| 10 | V Chernozhukov, W Newey, J Robins, and R Singh (2019) Double/de-biased machine learning of global and local parameters using regularized riesz representers | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 92 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.