Synthetic control · runs in your browser
Did the buyback move the price?
Pick a token that runs a buyback. The estimator builds a counterfactual out of a weighted blend of tokens that never ran one, fits that blend to the treated token's price path before the date you choose, and reports the gap that opens afterwards.
Then it does the same thing again pretending each donor was the treated one, so the gap can be read against the gaps you get by chance. The date is yours to move on purpose: sweeping it across every week in the window is what tells you whether a result at any single date means anything.
How well the blend tracked before the date. Large means nothing after it is interpretable.
The effect size. Dividing by pre-fit is what makes placebos comparable.
Share of placebos with a ratio at least this large.
Simplex constraint drives most weights to exactly zero.
Gap from the synthetic counterfactual
Orange is the treated token. Every grey line is one donor put through the identical procedure as if it had been treated on the same date. If orange sits inside the grey bundle after the marker, there is no effect to report.
Where the effect ranks
The same estimate at every possible treatment date
Effect size computed at every candidate date in the window. Blue dashes are dates a protocol actually changed its buyback policy. Click anywhere to move the estimator there.
Donor weights
The counterfactual is this blend, and nothing else.
What the estimator is doing
Minimising ‖y_pre − X_pre w‖² subject to w ≥ 0 and
Σw = 1, by projected gradient with Nesterov momentum and an exact
projection onto the simplex. Both constraints matter: without them the fit becomes an
unconstrained regression that interpolates anything and predicts nothing.
Inference is a rank test, not a parametric tail. With — donors the finest achievable p-value is —; a smaller number would be precision the design cannot deliver.
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