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 (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
