<ul><li>A new machine learning approach has been proposed for inferring causal variables of a target variable from observations.</li><li>The approach directly infers a set of causal factors without requiring full causal graph reconstruction, making it computationally efficient for large-scale systems.</li><li>The identified causal set includes potential regulators of the target variable, enabling efficient regulation in varying intervention costs and feasibility.</li><li>Empirical results demonstrate superior performance in identifying causal relationships within large-scale gene regulatory networks.</li></ul>

Large-Scale Targeted Cause Discovery with Data-Driven Learning

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