Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Presents a surrogacy framework for LLM-based A/B testing that identifies human average treatment effects through calibration under weaker conditions than full outcome equivalence.
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
New framework formalizes causal fairness for continuous protected attributes via path-specific derivatives and introduces a tuning algorithm for fair predictors.
A GEE-based stacked M-estimation framework merges propensity score and marginal structural models to directly compute the large-sample variance of the IPTW estimator from pilot data for prospective sample size planning, with bootstrap stabilization.
LGR samples balanced treatment assignments in high-dimensional experiments via continuous relaxation and SGLD, retaining valid inference through randomization tests while being orders of magnitude faster than prior methods.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
Presents a surrogacy framework for LLM-based A/B testing that identifies human average treatment effects through calibration under weaker conditions than full outcome equivalence.
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Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
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Tuning Derivatives for Causal Fairness in Machine Learning
New framework formalizes causal fairness for continuous protected attributes via path-specific derivatives and introduces a tuning algorithm for fair predictors.
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Estimator-Aligned Prospective Sample Size Determination for Designs Using Inverse Probability of Treatment Weighting
A GEE-based stacked M-estimation framework merges propensity score and marginal structural models to directly compute the large-sample variance of the IPTW estimator from pilot data for prospective sample size planning, with bootstrap stabilization.
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Langevin-Gradient Rerandomization
LGR samples balanced treatment assignments in high-dimensional experiments via continuous relaxation and SGLD, retaining valid inference through randomization tests while being orders of magnitude faster than prior methods.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
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Self-separated and self-connected models for mediator and outcome missingness in mediation analysis
Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.
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Estimating treatment duration effects via clone-censor-weight: a breast cancer case study
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
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A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
Compares six meta-learners (Cox/RSF risk models paired with elastic net/RF CATE models) via simulations differing in hazard complexity and censoring, and releases the R package crsurvlearners.