A new covariate selection strategy for high dimensional data in causal effect estimation with multivariate treatments
From MaRDI portal
Publication:6113075
DOI10.1016/j.jmva.2023.105207arXiv2303.09766MaRDI QIDQ6113075
Publication date: 8 August 2023
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2303.09766
Cites Work
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- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
- Robust inference on average treatment effects with possibly more covariates than observations
- Weak convergence and empirical processes. With applications to statistics
- High-dimensional confounding adjustment using continuous Spike and Slab priors
- Covariate selection for the nonparametric estimation of an average treatment effect
- Model selection and estimation in the Gaussian graphical model
- The central role of the propensity score in observational studies for causal effects
- The role of the propensity score in estimating dose-response functions
- Model‐averaged confounder adjustment for estimating multivariate exposure effects with linear regression
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- On Sure Screening with Multiple Responses
- Causal Inference With General Treatment Regimes
- Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties