Aleksander Molak - A Practical Guide to Causality in Python (For The Perplexed) | PyData NYC 2023

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With an average of 3.2 new papers published on Arxiv every day in 2022, causal inference has exploded in popularity, attracting large amount of talent and interest from top researchers and institutions including industry giants like Amazon or Microsoft.

There’s a very good reason for this upsurge in popularity. In our contemporary data culture we got accustomed to thinking that traditional machine learning methods can provide us with answers to any interesting business or scientific questions.

This view turns out to be incorrect. Many interesting business and scientific questions are causal in their nature and traditional machine learning methods are not suitable to address them.

In this talk, dedicated to data scientists and machine learning engineers with at least 3 years of experience, we’ll show why this is the case, we’ll introduce the fundamental tools for causal thinking and show how to translate them into code.

We’ll discuss a popular use case of churn prevention and demonstrate why only causal models should be used to solve it.

All in Python, repo included!

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