Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data
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Publication:6185139
DOI10.5705/ss.202022.0089arXiv2105.11362OpenAlexW3164163702MaRDI QIDQ6185139
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Publication date: 29 January 2024
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2105.11362
high-dimensional datacovariate-specific treatment effectdoubly robust confidence intervaldoubly robust point estimatormodel-assisted confidence interval
Cites Work
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- Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
- Statistical methods for dynamic treatment regimes. Reinforcement learning, causal inference, and personalized medicine
- Robust inference on average treatment effects with possibly more covariates than observations
- On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Translated from the Polish and edited by D. M. Dąbrowska and T. P. Speed
- Functional data analysis.
- Nonparametric likelihood and doubly robust estimating equations for marginal and nested structural models
- The central role of the propensity score in observational studies for causal effects
- Inference and missing data
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Program Evaluation and Causal Inference With High-Dimensional Data
- The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages
- Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
- A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
- Inference for treatment effect parameters in potentially misspecified high-dimensional models
- Debiased machine learning of conditional average treatment effects and other causal functions
- Double/debiased machine learning for treatment and structural parameters
- Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
- Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data
- Estimation of Optimal Individualized Treatment Rules Using a Covariate-Specific Treatment Effect Curve With High-Dimensional Covariates
- A new look at the statistical model identification
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