Dmitry Arkhangelsky, Guido W. Imbens
arXiv 20 Sep 2019 · Econometrics · publishedEconometrics Journal (2022) · 19 citations (OpenAlex)
arXiv:1909.09412 · PDF · DOI · OpenAlex · Extracted main text
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification where assumptions are made about the connection between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings but rarely used with panel data. We introduce different sets of assumptions that follow the two paths to identification and develop a doubly robust approach. We propose estimation methods that build on these identification strategies.
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