May 15, 2024, 4:45 a.m. | Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang

stat.ML updates on arXiv.org arxiv.org

arXiv:2202.07234v4 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv causal causal inference combination confounding data delay econ.em effects experimental however identification inference long-term replace stat.me stat.ml study treatment type via

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