AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
(2024).Identification and estimation of average causal effects in fixed effects logit models
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Derives sufficient statistics for feedback and heterogeneity in dynamic panel logit models, proves conditional likelihood identification is infeasible for Markov covariates, and proposes two assumptions to restore identification.
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Approximate Operator Inversion for Average Effects in Nonlinear Panel Models
AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.