The three major components of modern endurance training theory
Endurance athletes across the board are getting faster and stronger. In cycling, older generations of professional riders like Thomas de Gendt have talked ab...
Posts by Year
2023
2022
Causal faithfulness and the hot hand “fallacy”
Psychologists Gilovich, Vallone and Tversky famously argued that the concept of a “hot hand”—the phenomenon where a basketball player seems to enter a kind o...
Heterogeneous treatment effect estimation with observational data, pt. II
Two years ago I wrote a post on using Causal ML with observational data. I mentioned that, in developing Causal ML, we mostly had in mind the typical use cas...
Software and references for the talk on estimating individual treatment effects at KDD 2022
Below are some references for the talk I’m giving at KDD in the 1st Workshop on End-End Customer Journey Optimization. I will update this post with additiona...
2021
Jim Walmsley and the art of causal reasoning
Jim Walmsley is probably one of the best endurance athletes in the world. He is the three-time winner of the Western States 100-mile race and has a number of...
No Amount of Machine Learning Will Tell You Why a Training Plan Works
The Emergence of Big Data in Endurance Training Endurance training is ripe for disruption. In recent years, there’s been a sharp increase in the amount of da...
2020
Using the power curve to understand your cycling performance. Part 1: getting the data.
This is a series of blog posts in which I outline what I hope is a helpful way to use cycling power meter data to track your progress over time. In this firs...
Heterogeneous treatment effect estimation with observational data
When we released Causal ML, we didn’t expect many people to be interested in using the package with observational data. Historically, uplift modeling algorit...
New Beginnings
Welcome to my updated website! Here you’ll find up to date links to my research papers and other interesting resources mostly related to causal inference. I’...