An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
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
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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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.
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Federated Learning with Incomplete Data: When to Use Complete Cases and When to Weight
Framework for federated learning with missing data that identifies conditions favoring complete-case estimators over inverse-probability weighting and proposes a calibrated weighting approach consistent if at least one candidate model is correct.
-
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.
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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.
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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