ETAPS 2019
Sat 6 - Thu 11 April 2019 Prague, Czech Republic
Sun 7 Apr 2019 11:00 - 11:45 at S6 - Causal discovery methods

With the rapid accumulation of high variety high volume data, there is an increasing demand for computational methods that can perform effective and systematic knowledge discovery. In this talk, I will give an overview of computational causal discovery methods. These methods aim to discover the underlying data generation processes, i.e. causal mechanisms, from observational data, experimental data, and the combination of the two. The operating principles and the general analytical frameworks of computational causal discovery methods will be introduced, along with examples of their applications in biomedical sciences. I will also discuss causal feature selection, one important intersection of computational causal discovery and supervised learning predictive modeling. Causal feature selection is a class of feature selection methods that utilizes the causal relationships among the outcome and the predictors for feature selection. Causal feature selection methods produce predictor sets that result in robust, parsimonious, and interpretable predictive models.

Computational Causal Discovery and its Applications (Sisi Ma.pdf)1.96MiB

Sun 7 Apr

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