Fall 2019

Method and Theory in Causal Inference in the biological context Causal inference attempts to decipher the causal inference between the complex direct and indirect effects that occur through the complex relationships and interactions of biological units such as individuals (Ecology), proteins or genes. The course will cover the underlying theory, implementation methods of structural equation modelling and Bayesian networks and current approaches of statistics and Machine Learning. Students had the opportunity work on datasets and their own data in this workshop led by Dr. James Grace, Wetland and Aquatic Research Center, US Geological Survey.