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Found 3 papers, 3 papers with code
Adversarial Distribution Balancing for Counterfactual Reasoning
The development of causal prediction models is challenged by the fact that the outcome is only observable for the applied (factual) intervention and not for its alternatives (the so-called counterfactuals); in medicine we only know patients' survival for the administered drug and not for other therapeutic options.
FACT: Federated Adversarial Cross Training
We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain.
Federated Learning
Multi-Source Unsupervised Domain Adaptation
+2
BITES: Balanced Individual Treatment Effect for Survival data
Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes).
