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Causal thinking in data science

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Tommy J. and 3 others
Causal thinking in data science

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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

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.

COVID-19 safety measures

Event will be indoors
This event will also be broadcast on Zoom. Join us at https://us06web.zoom.us/j/88235260523?pwd=eGorS1Z0N1VwVFJLOElTT0hPNHZIQT09
The event host is instituting the above safety measures for this event. Meetup is not responsible for ensuring, and will not independently verify, that these precautions are followed.
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