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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.\n\nThis 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.\nIn this episode you will learn:\n\u2022 How causality is central to all applications of data science [4:32]\n\u2022 How correlation does not imply causation [11:12]\n\u2022 What is counterfactual and how to design research to infer causality from the results confidently [21:18]\n\u2022 Jennifer\u2019s favorite Bayesian and ML tools for making causal inferences within code [29:14]\n\u2022 Jennifer\u2019s new graphical user interface for making causal inferences without the need to write code [38:41]\n\u2022 Tips on learning more about causal inference [43:27]\n\u2022 Why multilevel models are useful [49:21]\n\nAdditional materials: www.superdatascience.com/607