Causal thinking in data science

This session will give attendees a gentle introduction to applying causal thinking and causal inference to data using Python. We'll cover the pitfalls of conducting an analysis using observational data, how causal inference can help get around these pitfalls, and briefly touch on a few examples of common, modern modeling approaches used to conduct causal inference.

Notes

• This is a hybrid in-person and streaming online event. Online link: https://us06web.zoom.us/j/88235260523?pwd=eGorS1Z0N1VwVFJLOElTT0hPNHZIQT09
• This event is on a Wednesday, not our usual Tuesday.

About the speaker, Roni Kobrosly
I'm a former epidemiology researcher who has spent about a decade employing causal modeling and inference. Since leaving the academic world, I've been loving my second life in the tech industry as a ML engineer, and more recently as the Head of Data Science at a medium-sized health tech company based in the DMV-area. I love mentoring junior data folks and explaining the magic of data and modeling to a non-technical audience. I'm also a contributor in the open-source community, as the author and maintainer of the `causal-curve` python package.