SDS 607: Inferring Causality

<p>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.</p><p>In this episode you will learn:<br/>• How causality is central to all applications of data science [4:32]<br/>• How correlation does not imply causation [11:12]<br/>• What is counterfactual and how to design research to infer causality from the results confidently [21:18]<br/>• Jennifer’s favorite Bayesian and ML tools for making causal inferences within code [29:14]<br/>• Jennifer’s new graphical user interface for making causal inferences without the need to write code [38:41]<br/>• Tips on learning more about causal inference [43:27]<br/>• Why multilevel models are useful [49:21]</p><p>Additional materials: <a href='https://gate.sc/?url=http%3A%2F%2Fwww.superdatascience.com%2F607&amp;token=60868a-1-1661966233174'>www.superdatascience.com/607</a></p>