PyMCon Web Series - Bayesian Causal Modeling - Thomas Wiecki

Welcome to another event in the PyMCon Web Series.
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Causal analysis is rapidly gaining popularity, but why? Machine learning methods might help us predict what's going to happen with great accuracy, but what's the value of that if it doesn't tell us what to do to achieve a desirable outcome? Without a causal understanding of the world, it's often impossible to identify which actions lead to a desired outcome.
Causal analysis is often embedded in a frequentist framework, which comes with some well-documented baggage. In this talk, we will present how we can super-charge PyMC for Bayesian Causal Analysis by using a powerful new feature: the do operator.
Content:
📖 Slides: docs.google.com/presentation/...
📝 Code: www.pymc-labs.com/blog-posts/...
🔗 You can see more details on the Discourse post: discourse.pymc.io/t/12912
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About the Speaker:
Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University, studying cognitive neuroscience.
🔗 Connect with Thomas:
👉 Website: www.pymc-labs.com
👉 Twitter: / twiecki
👉 GitHub: github.com/twiecki
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[@tarekelias463]

The memes are on point
Incredible explanation
10/10

[@adityosanjaya2182]

So cool!

[@adamfleischhacker8451]

Thanks for this video. In minute 20:42, can you explain where the normal parameters for "c" come from? My guess is they are only needed for the "do" operator and mu=0, sigma=1 are assumed to be known parameters of the future distribution of tv ad spend. Conjecturing further, if the distribution of tv ad spend was not assumed known, these parameters could be random variables as well. So, when the do-operator is used to get P(Y|c,do(z=0)), does the "c" here refer to the observed tv_spend or the observed output of a Normal(0,1) rv?