Inferring causal direction — as opposed to merely identifying correlations — is central to all real-world data science applications. World-leading expert and author on causality, Prof. Jennifer Hill, is our guest this week.

Jennifer:
• Is Professor of Applied Statistics at New York University, where she researches causality and practical applications of causal research, such as those that are vital to scientific development and government policies.
• Co-directs the NYU Masters in Applied Statistics and directs PRIISM (a center focused on impactful social applications of data science).
• With the renowned statistician Andrew Gelman, wrote the book "Data analysis using regression and multilevel/hierarchical models", an iconic textbook that has been cited over 15k times.
• Holds a PhD in Statistics from Harvard University.

Intended audience:
• Today’s episode largely contains content that will be of interest to anyone who’s keen to better understand the critical concept of causality.
• It also contains technical parts that will appeal primarily to practicing data scientists.

In this episode, Jennifer details:
• How causality is central to all applications of data science.
• How correlation does not imply causation.
• How to design research in order to confidently infer causality from the results.
• Her favorite Bayesian and machine learning tools for making causal inferences within code.
• ThinkCausal, her new graphical user interface for making causal inferences without the need to write code.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.