Synthesis of Causal Discovery and Machine Learning

Synthesis of Causal Discovery and Machine Learning

Presented at the 6th Annual BayesiaLab Conference in Chicago, November 1-2, 2018.


Fundamental research at the Software Engineering Institute at Carnegie Mellon University has raised questions surrounding the synthesis of both causal discovery and machine learning. Specifically, our research team has employed both the CMU open-source tool called Tetrad (for causal graph discovery from data) and BayesiaLab for supervised/unsupervised machine learning. This talk will briefly orient the audience to the Tetrad causal discovery process, share some contrasting results, and pose a list of open research questions regarding the potential synergy of the two technologies.

Presentation Video

Presentation Slides

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