Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
arXiv preprint arXiv:2110.14831 , year=
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A new weighted causal inference framework incorporates external controls via synthetic controls and balancing weights with multiple imputation and time-varying weights to reduce bias from treatment switching in RCTs for overall survival.
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.
Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.
citing papers explorer
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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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Leveraging External Controls for Treatment Switching in Randomized Controlled Trials: A Weighted Causal Inference Framework for Overall Survival
A new weighted causal inference framework incorporates external controls via synthetic controls and balancing weights with multiple imputation and time-varying weights to reduce bias from treatment switching in RCTs for overall survival.
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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.
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Local Balance Calibration for Nonparametric Propensity Score Estimation
Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
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Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
Balancing in debiased machine learning for causal effects should be guided by the Neyman orthogonal score, with covariate balancing as a special case appropriate only when regression errors depend solely on covariates.