AOI approximately inverts the likelihood mapping from fixed effects to outcomes to produce an estimator whose bias vanishes exponentially in T with double robustness.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
citing papers explorer
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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.
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Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.