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Found 4 papers, 2 papers with code
Towards Privacy-Aware Causal Structure Learning in Federated Setting
In this paper, we study a privacy-aware causal structure learning problem in the federated setting and propose a novel Federated PC (FedPC) algorithm with two new strategies for preserving data privacy without centralizing data.
Towards Efficient Local Causal Structure Learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data.
Learning causal representations for robust domain adaptation
In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.
Towards Efficient Local Causal Structure Learning
To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS.
