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UID:pretalx-scipy-2026-GB3N9K@pretalx.com
DTSTART;TZID=CST:20260713T133000
DTEND;TZID=CST:20260713T173000
DESCRIPTION:This tutorial session is intended to give attendees a gentle in
 troduction to applying causal thinking and inference using python. Causal 
 data analysis is very common in many academic domains (e.g. in social psyc
 hology\, epidemiology\, macroeconomics\, public policy research\, sociolog
 y\, and more) as well as in industry (all of the largest Silicon Valley te
 ch companies employ teams of scientists who answer business questions pure
 ly with causal inference methods).\n\nThe tutorial will involve a combinat
 ion of presentations with open Q&A and hands-on exercises contained in Mar
 imo 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 exam
 ples of common\, modern modeling approaches using the latest python causal
  inference frameworks (e.g. DoWhy). After the tutorial\, the attendees sho
 uld have a good foundational understanding of causality and the ability to
  confidently explore the topic on their own. Causal inference can be a ver
 y theory-heavy topic\, making it impenetrable to novices. In this tutorial
 \, we'll aim to take a more practical perspective on causal inference\, wh
 ile still occasionally touching on the theory.\n\nTutorial participants ar
 e 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 wi
 th the common python data stack: matplotlib\, numpy\, pandas\, and scikit-
 learn. Attendees should have some experience conducting classic machine le
 arning modeling using the scikit-learn API\, although having advanced mach
 ine learning expertise is absolutely not a prerequisite. A very basic unde
 rstanding of statistics would be helpful (e.g. understanding what a mean i
 s\, what confidence intervals represent).\n\nMaterials and installation in
 structions can be found here: https://github.com/ronikobrosly/scipy_2026_c
 ausal_inference_tutorial
DTSTAMP:20260715T023624Z
LOCATION:Other
SUMMARY:Introduction to Causal Inference (Room HSEC 3-110) - Roni Kobrosly
URL:https://pretalx.com/scipy-2026/talk/GB3N9K/
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