Copula parameterization of potential outcome dependence enables point identification, rate-doubly-robust estimation, and sensitivity analysis for causal effects with ordinal outcomes under unconfoundedness.
Double/debiased machine learning for treatment and structural parameters
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Active inference adapts label collection via ML uncertainty to deliver valid statistical inference with substantially fewer samples than standard non-adaptive methods across any data distribution.
The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.
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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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Active Statistical Inference
Active inference adapts label collection via ML uncertainty to deliver valid statistical inference with substantially fewer samples than standard non-adaptive methods across any data distribution.
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Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
The authors develop a two-stage orthogonal learning framework using graph neural networks to estimate heterogeneous direct and spillover causal effects on networks, along with bootstrap-based uncertainty quantification.