A new staggered DID framework identifies own-treatment, spillover, and total effects under network spillovers via prespecified exposure summaries and parallel trends among units with matching exposure.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
citing papers explorer
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Identification and Estimation of Staggered Difference-in-Differences with Network Spillovers
A new staggered DID framework identifies own-treatment, spillover, and total effects under network spillovers via prespecified exposure summaries and parallel trends among units with matching exposure.
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Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.