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Found 5 papers, 2 papers with code
Federated Learning of Causal Effects from Incomplete Observational Data
We introduce a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources.
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.
Adaptive Multi-Source Causal Inference
The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
Causal Modeling with Stochastic Confounders
This work extends causal inference with stochastic confounders.
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