Local Causal Discovery

Local causal discovery focuses on efficiently identifying causal relationships involving a specific target variable, rather than the entire causal graph, significantly reducing computational complexity. Current research emphasizes developing algorithms that leverage background knowledge, handle latent confounders and cyclic relationships (including feedback loops), and efficiently identify valid adjustment sets for unbiased causal effect estimation. These advancements are improving the feasibility and accuracy of causal inference in complex systems, with applications ranging from fair machine learning to policy analysis and treatment effect estimation in observational studies.