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Doubly robust identification for causal panel data models
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DOI：10.1093/ectj/utac019.png)
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Abstract
En 中文
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 double robust approach. We propose estimation methods that build on these identification strategies.
Keywords:
Fixed effects
cross-section data
clustering
causal effects
treatment effects
unconfoundedness