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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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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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.