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.
Page 19 of 26 Stephen R Cole and Constantine E Frangakis
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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.
citing papers explorer
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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.
-
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.