The Department of Statistics at Texas A&M University will host the five-day CBMS conference, “Foundations of Causal Graphical Models and Structure Discovery,” May 15–19 in College Station, Texas.
Causal discovery is becoming increasingly popular in both statistics and machine learning. It is a tool to generate causal hypotheses and bring new insights into existing association-based methods.
Kun Zhang from the departments of philosophy and machine learning at Carnegie Mellon University and Mohamed bin Zayed University of Artificial Intelligence will deliver 10 lectures on causal discovery. Tentatively, he will cover representations and usage of causal models, how causality is different from and connected to association, recent machine learning methods for causal discovery, and why and how the causal perspective helps in several learning tasks.
The conference registration fee is $25, and lunch will be provided.
Visit the conference website and register by April 30.
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