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Cited by 410Advanced Causal Inference TechniquesSpatial and Panel Data AnalysisEvaluation and Performance Assessment

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis

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Doudchenko, Nikolay, Imbens, Guido·2016·National Bureau of Economic Research
DOI 10.3386/w22791
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Abstract

In a seminal paper Abadie et al (2010) develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units.The method constructs a set of weights such that covariates and pre-treatment outcomes of the treated unit are approximately matched by a weighted average of control units.The weights are restricted to be nonnegative and sum to one, which allows the procedure to obtain the weights even when the number of lagged outcomes is modest relative to the number of control units, a setting that is not uncommon in applications.In the current paper we propose a more general class of synthetic control estimators that allows researchers to relax some of the restrictions in the ADH method.We allow the weights to be negative, do not necessarily restrict the sum of the weights, and allow for a permanent additive difference between the treated unit and the controls, similar to difference-in-difference procedures.The weights directly minimize the distance between the lagged outcomes for the treated and the control units, using regularization methods to deal with a potentially large number of possible control units.

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  1. Michihito Ando and F Sävje. Hypothesis testing with the synthetic control method. In European Economic Association and Econometric Society Meeting, pages 1-35, 2013.
  2. Joshua Angrist and Steve Pischke. Mostly Harmless Econometrics: An Empiricists' Companion. Princeton University Press, 2008.
  3. Susan Athey, Guido Imbens, and Stefan Wager. Efficient inference of average treatment effects in high dimensions via approximate residual balancing. arXiv preprint arXiv:1604.07125, 2016. · arXiv
  4. David Card. The impact of the mariel boatlift on the miami labor market. Industrial and Labor Relation, 43(2):245-257, 1990.
  5. Jens Hainmueller. Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1):25-46, 2012.
  6. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning. New York: Springer, 2009.
  7. Paul W Holland. Statistics and causal inference. Journal of the American Statistical Association, 81(396):945-970, 1986.
  8. Guido W Imbens and Donald B Rubin. Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press, 2015.
  9. Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5):688-701, 1974.
  10. Donald B Rubin. Matched sampling for causal effects. Cambridge University Press, 2006.
  11. Vadim von Brzeski, Matt Taddy, and David Draper. Causal inference in repeated observational studies: A case study of ebay product releases. arXiv preprint arXiv:1509.03940, 2015. · arXiv

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Metadata

Authors
Doudchenko, N., & Imbens, G.
Affiliations
  • National Bureau of Economic Research (US)
  • Stanford University (US)
ORCID
Imbens, Guido
Venue
National Bureau of Economic Research
Publisher
National Bureau of Economic Research
Published
2016-10
DOI
10.3386/w22791
OpenAlex
W2540009304
Cited by
410
References
22
Citations by year
2026: 32 · 2025: 54 · 2024: 51 · 2023: 44 · 2022: 44 · 2021: 72 · 2020: 41 · 2019: 34 · 2018: 23 · 2017: 11 · 2016: 3
Type
report
Language
en
Open access
Locations
  • National Bureau of Economic Research (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • National Bureau of Economic Research (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Social Sciences › Public Administration › Evaluation and Performance Assessment
Keywords
synthetic control method, treatment effect estimation, regularization, difference-in-differences, regression discontinuity design

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