Dr. Jennifer Hill, Professor of Applied Statistics at New York University, joins Jon 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 (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 [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer’s favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer’s new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607