The paper constructs minimax-bias estimators and uniformly valid confidence intervals for weighted estimands by bounding differences via parameter heterogeneity and weight distance.
Journal of the American Statistical Association , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4roles
background 1polarities
background 1representative citing papers
Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
citing papers explorer
-
Robust Inference for Weighted Estimands
The paper constructs minimax-bias estimators and uniformly valid confidence intervals for weighted estimands by bounding differences via parameter heterogeneity and weight distance.
-
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.
-
Transporting treatment effects by calibrating large-scale observational outcomes
A calibration procedure yields a weighted transported average treatment effect with asymptotically valid and efficient inference when experimental data grows slower than observational data, even without positivity or correct OLS specification.
- BAMIFun: Bayesian Multiple Imputation for Functional Data