5 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…
Constructing Confidence Intervals for Infinite-Dimensional Functional Parameters by Highly Adaptive Lasso
Wenxin Zhang, Junming Shi, Alan Hubbard +1
Estimating the conditional mean function is a central task in statistical learning. In this paper, we consider estimation and inference for a nonparametric class of real-valued cad…
HAL-Based Plug-in Estimation with Pointwise Asymptotic Normality of the Causal Dose-Response Curve
Junming Shi, Wenxin Zhang, Alan E. Hubbard +1
Estimating and obtaining reliable inference for the marginally adjusted causal dose-response curve for continuous treatments without relying on parametric assumptions is a well-kno…
Targeted Learning Estimation of Sampling Variance for Improved Inference
Yunwen Ji, Mark van der Laan, Alan Hubbard
For robust statistical inference it is crucial to obtain a good estimator of the variance of the proposed estimator of the statistical estimand. A commonly used estimator of the va…
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…