MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.
Journal of the American Statistical Association , volume=
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Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
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Synthetic Control Method with Mixed Frequency Data
MF-SCM constructs synthetic control weights from mixed-frequency data, proves the estimator achieves the lowest possible squared prediction error among averaging methods, and derives asymptotic inference for the average treatment effect.
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Causal Stability Selection
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.