ArticleJournal of the American Statistical Association2021
Prediction Intervals for Synthetic Control Methods.
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 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework.Journal of the American Statistical Association · 2026Article
- Synthetic control methods enable stronger causal inference using participatory science data in cities.Nature ecology & evolution · 2026Article
- Imperfect Synthetic Controls.Journal of applied econometrics (Chichester, England) · 2026Article
- Estimating endogenous treatments effects under long-range dependency without untreated controls.PloS one · 2026Article
- Real-world impact of nirsevimab immunisation against respiratory disease on emergency department attendances and admissions among infants: a multinational retrospective analysis.The Lancet regional health. Europe · 2025Article
- Doubly robust proximal synthetic controls.Biometrics · 2024Article
- The augmented synthetic control method in public health and biomedical research.Statistical methods in medical research · 2024Article
- Article
- Association Between Medicaid Managed Care Coverage of Substance Use Services and Treatment Utilization.JAMA health forum · 2022Article
- Regression discontinuity threshold optimization.PloS one · 2022Article
- Distributional conformal prediction.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation.Journal of the American Statistical Association · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our method allows for covariate adjustment and non-stationary data. The construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pre-treatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed. Accordingly, our proposed prediction intervals are constructed taking into account both sources of randomness. For implementation, we propose a simulation-based approach along with finite-sample-based probability bound arguments, naturally leading to principled sensitivity analysis methods. We illustrate the numerical performance of our methods using empirical applications and a small simulation study. Python, R and Stata software packages implementing our methodology are available.
Indexed as
Identifiers
What OpenQuestion holds
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.