Bayesian Additive Regression Trees: A Practitioners Guide with George Perrett - nyhackr Oct Meetup

Visit https://www.nyhackr.org to learn more and follow https://twitter.com/nyhackr Talk Title- Bayesian Additive Regression Trees: A Practitioners Guide Talk Description- Bayesian Additive Regression Trees (BART) is a powerful machine learning model with applications in prediction and causal inference problems. In this talk, I'll cover what BART is and its advantages compared to existing machine learning models and then cover how to fit your own BART models using the dbarts package. The focus of this talk will be on providing a useful "how to" guide for getting started with BART! Bio- George works as the director of the thinkCausal project at NYU. thinkCausal aims to make non-parametric causal inference more accessible and understandable to applied researchers. Before NYU he worked on evaluating the causal effects of educational interventions. He has earned degrees in applied statistics from NYU, Public Health from the University of Michigan and Psychology from the University of Wisconsin.