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.
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
Causal data analysis is very common in many academic domains and has been surging in popularity in the data industry over the last few years. In this tutorial I'll give attendees a gentle introduction to applying causal thinking and causal inference to data using python.
Attendees don't need any prior experience with causal inference or causal thinking. To make the most of the hands-on portion of the tutorial, attendees should have moderate experience with the modern python data stack: numpy, pandas, and scikit-learn. You will be able to walk away from this tutorial with a foundational understanding of causal inference and the ability to carry out your own causal analyses.
During the tutorial you will be split up into groups to work through two different exercise notebooks. All of the materials for the tutorial can be found here: https://github.com/ronikobrosly/pydata_nyc_2022