Bayesian Statistics and Causal Inference

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