  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.
