Doubly Robust Identification for Causal Panel Data Models
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Double-Robust Identification for Causal Panel Data Models
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abstract\singlespacing
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 relation between the treatment assignment and the unobserved confounders. Such strategies are common in cross-section settings but have rarely been used with panel data.
We introduce different sets of assumptions that follow the two paths to identification, and develop a double robust approach. We propose estimation methods that build on these identification strategies.