DCMA uses conditional generative models to recover and simulate interventional outcome distributions for distributional causal mediation effects, with derived error bounds.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.
citing papers explorer
-
Distributional Causal Mediation via Conditional Generative Modeling
DCMA uses conditional generative models to recover and simulate interventional outcome distributions for distributional causal mediation effects, with derived error bounds.
-
A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience
A meta-analytic framework estimates the resilience probability of a surrogate marker to the surrogate paradox in a new study by modeling deviations from functional relationships observed in completed trials.