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.
citation dossier
Journal of the American Statistical Association , volume=
2Pith papers citing it
2reference links
stat.MEtop field · 2 papers
UNVERDICTEDtop verdict bucket · 2 papers
why this work matters in Pith
Pith has found this work in 2 reviewed papers. Its strongest current cluster is stat.ME (2 papers). The largest review-status bucket among citing papers is UNVERDICTED (2 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
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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.