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arXiv AI By Mojtaba Eslami

Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment under Staggered Treatment Adoption

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The paper introduces Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment (RT‑SC‑DiD), a method for staggered treatment‑adoption studies that keeps the donor pool fixed by reallocating weights from exiting donors to similar surviving donors while applying a DiD baseline correction. It analyzes distortion from horizon‑by‑horizon re‑optimization, derives bounds on error propagation, and proposes diagnostics and a donor‑only placebo for tuning the transport penalty. Empirical simulations show that intermediate transport regularization reduces average RMSE compared to independent horizon‑specific estimation and strong anchoring, supporting the method’s bias‑variance trade‑off. "whyItMatters":"The method offers a principled way to stabilize synthetic‑control weights over time in staggered designs, potentially improving causal inference when donor support contracts as treatments roll out."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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