6 papers
Bridging Binarization: Causal Inference with Dichotomized Continuous Exposures
Kaitlyn J. Lee, Alan Hubbard, Alejandro Schuler
The average treatment effect (ATE) is a common parameter estimated in causal inference literature, but it is only defined for binary exposures. Thus, despite concerns raised by som…
Powering RCTs for marginal effects with GLMs using prognostic score adjustment
Emilie Højbjerre-Frandsen, Mark J. van der Laan, Alejandro Schuler
In randomized clinical trials (RCTs), the accurate estimation of marginal treatment effects is crucial for determining the efficacy of interventions. Enhancing the statistical powe…
A Non-Parametric Sensitivity Analysis for Bounding Bias in Hybrid Control Trials
Alissa Gordon, Emilie Højbjerre-Frandsen, Alejandro Schuler
We study hybrid control trials (HCTs), in which a randomized controlled trial (RCT) is augmented with external control patients. Existing approaches for HCTs typically assume condi…
"Within-trial" prognostic score adjustment is targeted maximum likelihood estimation
Emilie Højbjerre-Frandsen, Alejandro Schuler
Adjustment for ``super'' or ``prognostic'' composite covariates has become more popular in randomized trials recently. These prognostic covariates are often constructed from histor…
RieszBoost: Gradient Boosting for Riesz Regression
Kaitlyn J. Lee, Alejandro Schuler
Answering causal questions often involves estimating linear functionals of conditional expectations, such as the average treatment effect or the effect of a longitudinal modified t…
Data-Adaptive Identification of Effect Modifiers through Stochastic Shift Interventions and Cross-Validated Targeted Learning
David McCoy, Wenxin Zhang, Alan Hubbard +2
In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. F…