Adventures in Why

A Machine Learning Blog

Bob Wilson

Bob Wilson

Decision Scientist

Reality Labs


Bob Wilson is a decision scientist at Meta, where he focuses on marketing AR/VR devices such as the Meta Quest. Prior roles include Director of Data Science (Marketing) at Ticketmaster, and Director of Analytics at Tinder. His interests include causal inference, natural language processing, and convex optimization. When not tweaking his Emacs init file, Bob enjoys gardening, listening/singing along to Broadway musical soundtracks, and surfeiting on tacos.


  • Machine Learning
  • Convex Optimization
  • Statistics


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

    Stanford University

  • B.S. in Aerospace Engineering, 2008

    University of Illinois, Urbana-Champaign

Recent Musings

User Segmentation from Heterogeneous Treatment Effects

Imagine we are attempting to identify segments within an audience, perhaps so we can market to them more effectively through personalization. A common approach to doing so is to apply some kind of clustering algorithm (such as K-means) based on various user covariates.

Heterogeneous Treatment Effect Estimation: Function Approximation

A simple approach to heterogeneous treatment effect estimation relies on a difference in approximations to the outcome function among the two treatment groups. In this post, I derive the conditions under which this approach works.

Thoughts on Models with Regularization

Lately I’ve been reflecting on regularization. Early in my data science career I spent some time working with generalized additive models, but I started focusing more and more on traditional statistical methods. I am re-finding the value in regularization and expect to use more of it going forward.


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

Causal Inference and A/B Testing

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