Information-Theoretic Algorithms and Identifiability for Causal Graph Discovery
It is a task of widespread interest to learn the underlying causal structure for systems of random variables. Entropic Causal Inference is a recent framework for learning the causal graph between two variables from observational data (i.e., without experiments) by finding the information-theoretical...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/145148 |