Lasso adjustments of treatment effect estimates in randomized experiments
From MaRDI portal
Publication:2962335
DOI10.1073/pnas.1510506113zbMath1357.62098arXiv1507.03652OpenAlexW2963608360WikidataQ27320776 ScholiaQ27320776MaRDI QIDQ2962335
Adam Bloniarz, Cun-Hui Zhang, Hanzhong Liu, Bin Yu, Jasjeet Sekhon
Publication date: 16 February 2017
Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1507.03652
Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Optimal statistical designs (62K05)
Related Items
Randomization Tests for Weak Null Hypotheses in Randomized Experiments, Robust causal inference for incremental return on ad spend with randomized paired geo experiments, Model-Assisted Estimators with Auxiliary Functional Data, Statistical inference of heterogeneous treatment effect based on single-index model, Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes, Pair-Switching Rerandomization, Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies, The Generalized Oaxaca-Blinder Estimator, Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs, Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2 K Factorial Experiments, Regression-adjusted estimation of quantile treatment effects under covariate-adaptive randomizations, Neighborhood-based cross fitting approach to treatment effects with high-dimensional data, Regression adjustment for treatment effect with multicollinearity in high dimensions, Covariate-adjusted Fisher randomization tests for the average treatment effect, Causal inference: a missing data perspective, Rerandomization with diminishing covariate imbalance and diverging number of covariates
Cites Work
- Unnamed Item
- Statistics for high-dimensional data. Methods, theory and applications.
- Higher order inference on a treatment effect under low regularity conditions
- Randomization does not justify logistic regression
- 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
- Agnostic notes on regression adjustments to experimental data: reexamining Freedman's critique
- On regression adjustments to experimental data
- On regression adjustments in experiments with several treatments
- Statistics and Causal Inference
- Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, Using Sparse Linear Programming
- A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates