synthetic-control

Estimate causal policy effects using synthetic control by optimizing donor weights to match the pre-treatment outcome trajectory of a single treated unit in panel data, then computing the post-treatment gap between actual and synthetic outcomes.

Permutation placebo inference validates synthetic control results by running in-space placebo tests across donor units, computing RMSPE post/pre ratios, and generating a permutation p-value to assess the statistical significance of the estimated treatment effect.

Use synthetic control for policy evaluation when you have a single treated unit, a long pre-treatment window, and a donor pool of comparable untreated units, constructing a weighted counterfactual rather than relying on parallel trends assumptions.

Run in-space placebo tests by iteratively applying the synthetic control method to each untreated donor unit, calculating their RMSPE post/pre ratios, and deriving a permutation p-value from the resulting distribution to infer significance.

Yes, building a valid synthetic counterfactual requires long pre-treatment time series data to optimize donor weights that accurately minimize the pre-treatment outcome mismatch for the treated unit.

Yes, you can use pandas and numpy for synthetic control weight optimization in causal inference, applying nonnegative weights that sum to one to minimize pre-treatment outcome mismatch and estimate counterfactual outcomes.