Causality in Sequential Data

Inferring Causality is uniquely powerful when done with Sequential Data: data unfolding over time. Forecasting guru Dr. Sean Taylor — renowned for Prophet and now Motif Analytics co-founder — leads us through the topic.

Sean:
• Is Co-Founder and Chief Scientist of Motif Analytics, a startup that blends his deep expertise in causal modeling with sequential analytics.
• Previously worked as a Data Science Manager at Lyft.
• Also worked as a Research Scientist Manager at Facebook, where he led the development of the renowned open-source forecasting tool, Prophet.
• Holds a PhD in Information Systems from New York University and a BS in Economics from the University of Pennsylvania.

Today’s episode gets deep into the weeds on occasion, particularly when discussing making causal inferences, but most of the episode will resonate with any curious listener.

In this episode, Sean:
• Publicly unveils his new venture, filling us in on why now was the right time for him to co-found and lead data science at an ML startup.
• Details what causal modeling is, why every data scientist should be familiar with it, and how it can make a real-world impact, with many illustrative examples from his time at Lyft.
• Fills us in on the infrastructure and teams required for large-scale causal experimentation.
• Covers how causal modeling and forecasting can’t be fully automated today as it requires humans to make assumptions, but also how humans can make these assumptions in a more informed manner thanks to data visualizations.
• Explains what the field of Information Systems is and, having conducted several hundred interviews, what he looks for in the data scientists he hires.

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

Causal Machine Learning

Causal ML is today's focus with Dr. Emre Kiciman — Senior Principal Researcher at Microsoft, developer of the DoWhy causal modeling library for Python, and a leader in applying causal research to social sciences.

Emre:
• Has worked within prestigious Microsoft Research for over 17 years.
• Leads Microsoft’s research on Causal Machine Learning.
• Leads development of the DoWhy open-source causal modeling library for Python (part of the PyWhy GitHub project).
• Pioneered the use of social media data to answer causal questions in the social sciences, such as with respect to physical and mental health.
• Has published 100+ papers and been cited 8000+ times.
• Holds a PhD in Computer Science from Stanford University.

Today’s episode is relatively technical, so will probably appeal primarily to folks with technical backgrounds like data scientists, ML engineers, and software developers.

In this episode, Emre details:
• What Causal ML is and how it’s different from "correlational" ML.
• The four key steps of causal inference and how they impact ML.
• The types of data that are most amenable to causal methods and those that aren’t yet… but may be soon.
• Exciting real-world applications of Causal ML.
• The software tools he most highly recommends.
• What he looks for in the data science researchers he hires.

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