A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Extends nonparanormal adjusted marginal inference into a joint model that preserves marginal treatment effect interpretability while estimating heterogeneity for multiple outcome types.
citing papers explorer
-
Separable Effects in Four-Arm and Two-Arm Designs
A methodological framework for separable effects analysis that distinguishes four-arm and two-arm designs, with EIF-based estimation and falsification tests.
-
Joint Estimation of Marginal and Heterogeneous Treatment Effects
Extends nonparanormal adjusted marginal inference into a joint model that preserves marginal treatment effect interpretability while estimating heterogeneity for multiple outcome types.