Synthetic Control Method (SCM)
Also known as: synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.
Key highlights
- Provides a transparent, data-driven counterfactual for N=1 situations where no single comparison unit is available.
- Considered the gold standard for evaluating large policy interventions, natural experiments, and comparative case studies.
- The convexity constraint guards against extrapolation: the synthetic unit stays within the range of the observed donor units.
Intuition
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
When to use it
Use SCM when a single unit is exposed to an intervention and you observe it alongside a pool of comparable untreated donor units across a panel or time series. It requires a sufficiently long pre-treatment window (at least about 20 periods) so the synthetic unit can be matched to the treated unit's history. Key assumptions are: a long enough pre-treatment period for good fit, the presence of similar units in the donor pool, no interference (donor units must be unaffected by the treatment), and weights estimated under the convex-combination constraint. It is less suitable when the donor pool is too small or the pre-treatment fit is poor.
Strengths & limitations
- Provides a transparent, data-driven counterfactual for N=1 situations where no single comparison unit is available.
- Considered the gold standard for evaluating large policy interventions, natural experiments, and comparative case studies.
- The convexity constraint guards against extrapolation: the synthetic unit stays within the range of the observed donor units.
- Needs a long pre-treatment period; with fewer than about 20 periods the weighted combination cannot reproduce the treated unit's pre-period history.
- A poor pre-treatment fit (high RMSPE) makes the synthetic counterfactual unreliable.
- Requires a donor pool of genuinely comparable units that are not themselves affected by the treatment (no interference).
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Applications
This section is available to Pro members. Upgrade to Pro
Frequently asked
How is the synthetic control different from a simple control group?
Instead of picking one comparison unit, SCM constructs a weighted blend of several untreated donor units so that the combination matches the treated unit's pre-treatment characteristics and outcome path. This data-driven counterfactual is usually closer to the treated unit than any single comparison unit would be.
What does the pre-treatment fit tell me?
It is the core diagnostic. A close pre-treatment fit (low root-mean-square prediction error, RMSPE) means the synthetic unit reproduces the treated unit's history well, so the post-treatment gap can be read as a treatment effect. A poor fit signals that the counterfactual is unreliable.
How many periods and donor units do I need?
You need a reasonably long pre-treatment window (at least about 20 periods) and a pool of genuinely comparable untreated units. Too few periods or too small a donor pool means the convex combination cannot match the treated unit, and the method should give way to an interrupted time series design.
What should I do if the donor pool is too small?
When the donor pool cannot reproduce the treated unit's pre-treatment trajectory, switch to an interrupted time series analysis, which estimates the intervention effect from the treated unit's own before-and-after series rather than from a weighted comparison.
Sources
- 1.Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
- 2.Abadie, A. (2021). Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), 391-425.
You have read it. What now?
Cite this page
ScholarGate. (2026, June 1). Synthetic Control. ScholarGate. https://scholargate.app/causal-inference/synthetic-control