Dear friends,

Welcome to the July 26th edition of the Sunday Briefing. This weeks issue is filled up to the brim with exciting content.

We continue our Causal Inference journey. We just published the fourth installment of the Causal Inference Series, where we dive into Structural Causal Models, the fundamental concept we'll build on going for the rest of the book. We hope you find it useful and look forward to your insightful comments. We also refreshed the Causality GitHub repository with links to all the posts of the series and full Binder support so that you can easily run the code in the cloud.

A new post on the CoVID-19 series post in currently in the works (be on the look out for it later this week) and in the mean time, you can catch up on our previous posts and follow along with the code hosted on our Epidemiology101 GitHub repository.

We're also proud to announce a YouTube playlist aggregating all the videos of the week. Subscribe today for an easy way to keep track of one of our most popular features and don't forget to follow us on YouTube.

In our regularly scheduled content, we explore Uber’s Hexagonal Hierarchical Spatial Index, the fundamentals of NumPy and Machine Learning Research at Apple. We also continue our exploration of causality with posts on Causal Reinforcement Learning and on how we might use causal graphs to understand missingness.

On the academic front, we have a curated collection of COVID-19 online datasets, an exploration of how Mathematical models guide pandemic response and A Mathematical Theory of Attention. We round up the week with a look at Problems of Representativeness and Data Reliability in the Google Books Ngram dataset and Graph-based process mining

Finally, the video of the week we have an interview with Michael Jordan (no, not that one) where he covers Machine Learning, Recommender Systems, and the Future of AI.

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Today, more than ever,
Semper discentes,

The D4S team