Adventures in Why

A Machine Learning Blog

Bob Wilson

Bob Wilson

Data Scientist



Bob Wilson (he/him/his) is a data scientist at Netflix, where he helps entertain the world by improving content quality. Prior roles include Marketing Analytics at Meta Reality Labs, Director of Data Science (Marketing) at Ticketmaster, and Director of Analytics at Tinder. His interests include causal inference and convex optimization. When not tweaking his Emacs init file, Bob enjoys gardening, listening/singing along to Broadway musical soundtracks, and surfeiting on tacos.


  • Causal Inference
  • Convex Optimization
  • Theoretical Statistics


  • M.S.E.E. in Machine Learning, 2013

    Stanford University

  • B.S. in Aerospace Engineering, 2008

    University of Illinois, Urbana-Champaign

Recent Musings

Tests with One-Sided Noncompliance

Table of Contents Introduction As Treated, Per Protocol, and Intent to Treat Potential Outcomes Notation Instrumental Variables Dose-Response Models Conclusions and Further Reading References Introduction Tech companies spoil data scientists. It’s so easy for us to A/B test everything.

Eglot+Tree-Sitter in Emacs 29

I’ve been an Emacs user for about 15 years, and for the most part I use Emacs for org-mode and python development. I’ve happily used Jorgen Schäfer’s elpy as the core of my python development workflow for the last 5 years or so, and I’ve been happy with it.

Compiling Emacs 29 With Tree-Sitter

I started a new job recently and took the opportunity to install a new version of Emacs. Emacs 29 includes tree-sitter and built-in eglot support, which I’ll write about some other time.


A/B Testing

Calculators for planning and analyzing A/B tests


Generalized Additive Models in Python


Orbit Propagator in Python


Homebrewed Beer Calculator

Unit Parser

Unit Parser and Conversions in Python

Other Papers

Star Identification via Computer Vision Techniques

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A Discussion of Relativistic Phenomena and Construction of Spacetime Diagrams

We discuss how the Special Theory of Relativity proceeds from the absence of an absolute definition of stationarity, as well as the observation that light travels at the same speed in all reference frames. Some interesting phenomena follow: two observers in relative motion cannot always agree on the length of an object, the time between two events, or even in what order the events occurred.

Recent & Upcoming Talks

Beyond A/B Testing: Getting More from Experiments

In my journey to improve the design and analysis of A/B tests, I have turned to the literature on observational causal inference. Along the way, I have learned several techniques to level up experiments. These techniques include tests of equivalence and non-inferiority, closed testing procedures, methods for non-compliance, and heterogeneous treatment effects.

Causal Inference and A/B Testing

Interana invited me to give a talk on A/B testing and analytics at Tinder.