Sample Efficient Active Learning of Causal Trees
Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adsera, Guy Bresler
–Neural Information Processing Systems
Causal discovery from observational and interventional data is a fundamental problem and prevalent in multiple areas of science and engineering (Pearl, 2009; Spirtes et al., 2000; Peters et al., 2017). Learning the underlying causal mechanisms is essential for policy design.
Neural Information Processing Systems
Oct-2-2025, 20:11:34 GMT
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