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Counterfactual Treatment Estimation Model by j.lowhorn is a document available to read on EtoBox.

The paper introduces the Counterfactual Recurrent Network (CRN), a novel model designed to estimate treatment effects over time using observational patient data. CRN employs domain adversarial training to create treatment invariant representations that mitigate bias from time-varying confounders, enabling accurate counterfactual predictions. Experiments demonstrate that CRN outperforms existing methods in estimating treatment outcomes and determining optimal treatment timing in a simulated tumor growth mode

Author

j.lowhorn

Language

EN