A Penalized Synthetic Control Estimator for Disaggregated Data

DOI10.1080/01621459.2021.1971535zbMath1506.62243OpenAlexW3198029582MaRDI QIDQ5881965

Jérémy L'Hour, Alberto Abadie

Publication date: 14 March 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2021.1971535

zbMATH Keywords

observational studiestreatment effectscausal inference

Mathematics Subject Classification ID

Causal inference from observational studies (62D20)

Related Items (3)

Prediction Intervals for Synthetic Control Methods ⋮ The policy is always greener: impact heterogeneity of Covid-19 vaccination lotteries in the US. ⋮ Confidence intervals of treatment effects in panel data models with interactive fixed effects

Cites Work

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• Does matching overcome LaLonde's critique of nonexperimental estimators?
• Optimality of the Delaunay triangulation in \(\mathbb{R}^ d\)
• Design-based analysis in difference-in-differences settings with staggered adoption
• Recursive partitioning for heterogeneous causal effects
• The central role of the propensity score in observational studies for causal effects
• Causal Inference for Statistics, Social, and Biomedical Sciences
• Incremental construction of the delaunay triangulation and the delaunay graph in medium dimension
• Bias-Corrected Matching Estimators for Average Treatment Effects
• Large Sample Properties of Matching Estimators for Average Treatment Effects
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Matrix Completion Methods for Causal Panel Data Models
• The Augmented Synthetic Control Method
• An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

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