<ul data-eligibleForWebStory="false"><li>Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown.</li><li>A new algorithm called RelFCI has been proposed to address the challenge of learning causal models with latent confounders from relational data.</li><li>RelFCI builds upon existing causal inference and relational causal discovery algorithms to provide sound and complete causal discovery in relational domains.</li><li>Experimental results show the effectiveness of RelFCI in identifying the correct causal structure in relational causal models with latent confounders.</li></ul>

Relational Causal Discovery with Latent Confounders

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