Sliding into Causal Inference, with Python

Explore the fascinating world of causal inference and learn how to simulate parallel realities to answer intriguing "what if" questions in this 32-minute conference talk from EuroPython 2021. Gain an intuitive understanding of key tools like Differences-in-Differences, Propensity Score Methods, and Synthetic Controls, all implemented using Python. Discover how to identify causal inference problems, apply various techniques, and further your learning in this field. Delve into the Fundamental Problem of Causal Inference, understand the importance of randomized controlled experiments, and get a glimpse of advanced topics like Directed Acyclic Graphs (DAGs). By the end, acquire knowledge of Python tools for causal inference and valuable resources to continue your journey in this exciting area of data science.

Intro
Agenda
Randomization
Network Effects
Order Distribution
propensity score matching
free delivery
synthetic control
output
what next