2 citations · 4 across the 8 of their papers we have counts for
9 papers
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +7
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy va…
Simple Covariate Adjustment for Many Estimands Using Stable Balancing Weights
Kayla Irish, José Zubizarreta, Alex Luedtke
Covariate adjustment can improve precision in estimating treatment effects in clinical trials, and regulatory guidance increasingly encourages its implementation. Despite its benef…
Sequentially Doubly Robust Estimation of Conditional Survival Probability with Time-Varying Covariates
Hongxiang Qiu, Marco Carone, Alex Luedtke +1
It is often of interest to study the association between covariates and the cumulative incidence of a right-censored time-to-event outcome. When time-varying covariates are measure…
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
Lars van der Laan, Aurelien Bibaut, Nathan Kallus +1
We develop a unified framework for automatic debiased machine learning (autoDML) for inference on a broad class of statistical parameters. The framework applies to any smooth funct…
Stabilized Inverse Probability Weighting via Isotonic Calibration
Lars van der Laan, Ziming Lin, Marco Carone +1
Inverse weighting with an estimated propensity score is widely used by estimation methods in causal inference to adjust for confounding bias. However, directly inverting propensity…
Doubly robust inference via calibration
Lars van der Laan, Alex Luedtke, Marco Carone
Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While consistency requires only one of two nui…
Propensity Score Augmentation in Matching-based Estimation of Causal Effects
Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke
When assessing the causal effect of a binary exposure using observational data, confounder imbalance across exposure arms must be addressed. Matching methods, including propensity…
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
Lars van der Laan, Marco Carone, Alex Luedtke
We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and condition…
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
Nina Galanter, Marco Carone, Ronald C. Kessler +1
Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that…