MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.
Optimal Learning from the Doob-Dynkin lemma
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abstract
The Doob-Dynkin Lemma gives conditions on two functions $X$ and $Y$ that ensure existence of a function ${\phi}$ so that $X = {\phi} \circ Y$. This communication proves different versions of the Doob-Dynkin Lemma, and shows how it is related to optimal statistical learning algorithms. Keywords and phrases: Improper prior, Descriptive set theory, Conditional Monte Carlo, Fiducial, Machine learning, Complex data.
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stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis
MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.