Mar 17, 2023 · In this paper, we propose a new covariate selection strategy called double screening prior adaptive lasso (DSPAL) to select confounders and ...
Through a simulation study, we demonstrate that the proposed approach selects all confounders and predictors consistently and estimates the multivariate ...
Mar 17, 2023 · Our theoretical analyses show that the proposed procedure enjoys the sure screening property, the ranking consistency property and the variable ...
Through a simulation study, we demonstrate that the proposed approach selects all confounders and predictors consistently and estimates the multivariate ...
Mar 17, 2023 · is proposed to estimate causal effect of multivariate continuous treatments with high or even ultra-high dimensional covariates. The DSPAL ...
Covariance. Article. A new covariate selection strategy for high dimensional data in causal effect estimation with multivariate treatments. June 2023; Journal ...
On High-Dimensional Covariate Adjustment for Estimating Causal ...
www.ncbi.nlm.nih.gov › PMC10153578
Apr 1, 2024 · In this work, we effectively utilize high-dimensional covariate information to reduce the variance in ACE estimation under randomization trials.
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Several methods have been proposed to estimate causal effects with multi- ple treatments from observational data. We provide an overview of these methods ...
Oct 1, 2023 · What is the recommended technique for selecting variables for a causal inference model? Let's say we're estimating an average treatment ...
Jun 3, 2020 · Summary. We propose a robust method to estimate the average treatment effects in observational studies when the number of potential ...