07-10, 13:30–17:30 (America/Chicago), Classroom 203
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.
The tutorial will involve a combination of presentations with open Q&A and hands-on exercises contained in Google Colab 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 used to conduct causal inference (propensity score matching, estimating causal curves, g-computation, and double ML). 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).
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).
Installation Instructions –https://github.com/ronikobrosly/scipy_2023_causal_inference_tutorial
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).