Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Title resolution pending
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
stat.ME 7years
2026 7roles
background 1polarities
background 1representative citing papers
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
The MQIV model identifies the ATT via a modified Wald ratio under a multiplicative treatment model that permits direct effects of the quasi-instrument on the outcome.
citing papers explorer
-
Causal inference with ordinal outcomes: copula-based identification, estimation and sensitivity analysis
Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
-
Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
-
Augmented transfer regression learning for completely missing covariates
A doubly robust, asymptotically normal estimator for regression with completely missing covariates across populations, combining importance weighting and moment imputation under a sub-population shift assumption.
-
The Multiplicative Quasi-Instrumental Variable Model
The MQIV model identifies the ATT via a modified Wald ratio under a multiplicative treatment model that permits direct effects of the quasi-instrument on the outcome.
- Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
- Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
- Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding