SDS 607: Inferring Causality

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

Om Podcasten

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.