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.
Difference-in-differences with variation in treatment timing
3 Pith papers cite this work. Polarity classification is still indexing.
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Firmware removal of ungoverned mode on e-scooters lowers both harsh-acceleration and harsh-deceleration event rates through a purely mechanical channel, validated by causal DiD estimates on 19.5 million trips.
Emulating stepped-wedge cluster randomized trials in the target trial emulation framework provides a conceptual structure for evaluating health policies with staggered adoption in observational and quasi-experimental studies.
citing papers explorer
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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.
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Do E-Scooter Speed Governance Policies Reduce Harsh Acceleration and Deceleration? Evidence from 19.5 Million Trips Around a Regulatory Ban
Firmware removal of ungoverned mode on e-scooters lowers both harsh-acceleration and harsh-deceleration event rates through a purely mechanical channel, validated by causal DiD estimates on 19.5 million trips.
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Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions
Emulating stepped-wedge cluster randomized trials in the target trial emulation framework provides a conceptual structure for evaluating health policies with staggered adoption in observational and quasi-experimental studies.