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, AI/ML engineer, and more recently as a Director of Data Science Observability at Capital One. I love mentoring junior data folks and explaining the magic of data analysis and modeling to non-technical audience. I am also a proud member of the open-source community!
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Session
This tutorial session is intended to give attendees a gentle introduction to applying causal thinking and inference using python. Causal data analysis is very common in many academic domains (e.g. in social psychology, epidemiology, macroeconomics, public policy research, sociology, and more) 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 Marimo notebooks. This session will cover the difference between correlation and causation, the pitfalls of conducting an analysis using observational data, how causal inference can help get around these pitfalls, and examples of common, modern modeling approaches using the latest python causal inference frameworks (e.g. DoWhy). After the tutorial, the attendees should have a good foundational understanding of causality and the ability to confidently explore the topic on their own. Causal inference can be a very theory-heavy topic, making it impenetrable to novices. In this tutorial, we'll aim to take a more practical perspective on causal inference, while still occasionally touching on the theory.
Tutorial participants are not expected to be familiar with causal inference before attending, but we hope they have an earnest curiosity to learn about it! To get the most out of the session, the participants ought to have experience working with the common python data stack: matplotlib, numpy, pandas, and scikit-learn. Attendees should have some experience conducting classic machine learning modeling using the scikit-learn API, although having advanced machine learning expertise is absolutely not a prerequisite. A very basic understanding of statistics would be helpful (e.g. understanding what a mean is, what confidence intervals represent).
Materials and installation instructions can be found here: https://github.com/ronikobrosly/scipy_2026_causal_inference_tutorial