#DataScience #CausalInference #BayesianStatistics We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. This episode is brought to you by Pachyderm, the leader in data versioning and MLOps pipelines and by Zencastr, http://zen.ai/sds, the easiest way to make high-quality podcasts. In this episode you will learn: • How causality is central to all applications of data science [3:01] • How correlation does not imply causation [09:40] • What is counterfactual and how to design research to infer causality from the results confidently [19:40] • Jennifer’s favorite Bayesian and ML tools for making causal inferences within code [27:42] • Jennifer’s new graphical user interface for making causal inferences without the need to write code [37:59] • Tips on learning more about causal inference [42:45] • Why multilevel models are useful [48:39] Additional materials: https://www.superdatascience.com/607