ArticleJournal of the American Statistical Association2021
Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Bayesian shrinkage priors for penalized synthetic control estimators in the presence of spillovers.Biometrics · 2026Article
- National pilot of innovative early years skill mix working in five Start for Life sites in England: study protocol.Primary health care research & development · 2026Article
- Game, Set, and Match: A Scoping Review of Matching Characteristics for Control and Intervention Groups in Adaptive Behavioral Interventions for Physical Activity or Healthy Eating Designs for Populations with Overweight and Obesity.Behavioral medicine (Washington, D.C.) · 2026Review
- Imperfect Synthetic Controls.Journal of applied econometrics (Chichester, England) · 2026Article
- Synthetic Regressing Control.Observational studies · 2026Article
- Impact of vaccine mandates and removals on COVID-19 vaccine uptake in Australia and international comparators: a study protocol.BMJ open · 2025Article
- The augmented synthetic control method in public health and biomedical research.Statistical methods in medical research · 2024Article
- Prediction Intervals for Synthetic Control Methods.Journal of the American Statistical Association · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve trade-offs between interpolation and extrapolation bias. We use a series of empirically-based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we apply these estimators to examine the economic costs of conflicts in the context of Spain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.