An alternative to synthetic control for models with many covariates under sparsity

DOI10.1007/978-3-031-30114-8_12arXiv2005.12225OpenAlexW3124737516MaRDI QIDQ6200194

Alexandre B. Tsybakov, Xavier D'Haultfœuille, Marianne Bléhaut, Jérémy L'Hour

Publication date: 22 March 2024

Published in: Foundations of Modern Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.12225

zbMATH Keywords

high-dimensiontreatment effectcovariate balancingsynthetic control

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data
• Does matching overcome LaLonde's critique of nonexperimental estimators?
• Estimation and testing under sparsity. École d'Été de Probabilités de Saint-Flour XLV -- 2015
• Sparse estimators and the oracle property, or the return of Hodges' estimator
• Robust inference on average treatment effects with possibly more covariates than observations
• The Lasso problem and uniqueness
• Least squares after model selection in high-dimensional sparse models
• Slope meets Lasso: improved oracle bounds and optimality
• Simultaneous analysis of Lasso and Dantzig selector
• Sparsity oracle inequalities for the Lasso
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• Reconstruction From Anisotropic Random Measurements
• Irregular Identification, Support Conditions, and Inverse Weight Estimation
• Self-Normalized Processes
• GUEST EDITORS' EDITORIAL: RECENT DEVELOPMENTS IN MODEL SELECTION AND RELATED AREAS
• Calibration Estimators in Survey Sampling
• Inverse Probability Tilting for Moment Condition Models with Missing Data
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• Program Evaluation and Causal Inference With High-Dimensional Data
• Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
• Double/debiased machine learning for treatment and structural parameters
• Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
• MODEL SELECTION AND INFERENCE: FACTS AND FICTION
• Covariate Balancing Propensity Score
• The Augmented Synthetic Control Method

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