Synthetic Control Method
A causal inference method proposed by Abadie, Diamond, and Hainmueller in 2010. It constructs a counterfactual trajectory by forming a weighted combination of multiple control units, does not require the parallel trends assumption, and is suited to settings with few treated units and many control units.
Timeline (last 90 days)
The synthetic control method was proposed by Abadie, Diamond, and Hainmueller in 2010, and its core idea is to fit a counterfactual trajectory for the treatment group using a weighted combination of multiple control units.
The synthetic control method does not require the parallel trends assumption; instead, it constructs a separate match for each treated unit and tends to have better applicability in scenarios where the number of treated units is small and the number of control units is large.
In the placebo test, 90% of the synthetic control method estimates narrowed to between -7% and +9%, with RMSE dropping to 4.9 percentage points, more than one-third lower than DiD.
Applying the synthetic control method to bookings per thousand people rather than raw booking volumes allows the model to track the treatment group more closely in the pre-treatment period, with RMSE dropping to 4.4 percentage points.
During the actual treatment period, the synthetic control method (raw bookings) estimated 7%, the synthetic control method (per-thousand-population basis) estimated 9.6%, and the difference-in-differences method estimated 24%, with the first two being closer to the true effect of 10%.
The limitation of the synthetic control method is that fit quality degrades when the number of treated units is large or when control group coverage is insufficient.
All Facts (6)
The synthetic control method was proposed by Abadie, Diamond, and Hainmueller in 2010, and its core idea is to fit a counterfactual trajectory for the treatment group using a weighted combination of multiple control units.
50%UnverifiedThe synthetic control method does not require the parallel trends assumption; instead, it constructs a separate match for each treated unit and tends to have better applicability in scenarios where the number of treated units is small and the number of control units is large.
50%UnverifiedIn the placebo test, 90% of the synthetic control method estimates narrowed to between -7% and +9%, with RMSE dropping to 4.9 percentage points, more than one-third lower than DiD.
50%UnverifiedApplying the synthetic control method to bookings per thousand people rather than raw booking volumes allows the model to track the treatment group more closely in the pre-treatment period, with RMSE dropping to 4.4 percentage points.
50%UnverifiedDuring the actual treatment period, the synthetic control method (raw bookings) estimated 7%, the synthetic control method (per-thousand-population basis) estimated 9.6%, and the difference-in-differences method estimated 24%, with the first two being closer to the true effect of 10%.
50%UnverifiedThe limitation of the synthetic control method is that fit quality degrades when the number of treated units is large or when control group coverage is insufficient.
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