Roni Kobrosly
I am a former epidemiology researcher who has spent approximately a decade employing causal modeling and inference. The bulk of my academic career was spent conducting data analyses to estimate the population-level effects of harmful environment exposures, when traditional randomized experiments were infeasible or unethical. During this time, I taught a couple undergraduate epidemiology courses, once of which involved a sizable introduction to causal thinking. I've also presented many one-off departmental presentations and at a few epidemiology conferences on causal inference in both cases.
Since leaving the academic world, I've been loving my second life in the tech industry as a data scientist, ML engineer, and more recently as the Head of Data Science at a medium-sized health tech company based in Washington DC. I love mentoring junior data folks and explaining the magic of data analysis and modeling to non-technical audience.
I also am a member of the open-source community, being the author and maintainer of the causal-curve python package. This package provides a set of tools for estimating the causal impact of continuous/non-binary treatments (e.g. estimating the causal impact of a neighborhood's income inequality on local crime, or understanding the causal effect of increasing a product's price on conversion rates).
Sessions
This tutorial session is intended to give attendees a gentle introduction to applying causal thinking and causal inference to data using python. Causal data analysis is very common in many academic domains (e.g. in social psychology, epidemiology, macroeconomics, etc) as well as in industry (all of the largest Silicon Valley tech companies employ teams of scientists who answer business questions purely with causal inference methods). The tutorial will involve a combination of presentations with open Q&A and hands-on exercises contained in Google Colab notebooks.