Establishes identification of the full data law with hidden outcomes via proxies and develops multiply robust influence function estimators for causal effects.
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Under a constant-coefficient structural model and exact conditional calibration of p, the latent group coefficient τ is point-identified as the covariance of (2p-1) with the partialled outcome divided by twice the residual variance of p given X.
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.
Compares bridge equation and array decomposition proxy methods for causal identification, highlighting differences in model restrictions and scope of applicability.
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Proximal Causal Inference for Hidden Outcomes
Establishes identification of the full data law with hidden outcomes via proxies and develops multiply robust influence function estimators for causal effects.
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Identification of Latent Group Effects under Conditional Calibration
Under a constant-coefficient structural model and exact conditional calibration of p, the latent group coefficient τ is point-identified as the covariance of (2p-1) with the partialled outcome divided by twice the residual variance of p given X.
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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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Comparing Two Proxy Methods for Causal Identification
Compares bridge equation and array decomposition proxy methods for causal identification, highlighting differences in model restrictions and scope of applicability.