Causal Machine Learning is off to a good start, despite some issues

Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.

September 12, 2022

Analysis strategies in online A/B experiments: Intention-to-treat, per-protocol, and other lessons from clinical trials

Epidemiologists analyse clinical trials to estimate the intention-to-treat and per-protocol effects. This post applies their strategies to online experiments.

January 14, 2022

My work with Automattic

Back-dated meta-post that gathers my posts on Automattic blogs into a summary of the work I’ve done with the company.

October 7, 2021

Some highlights from 2020

Sharing remote teamwork insights, my climate & sustainability activism, Reef Life Survey publications, and progress on Automattic’s Experimentation Platform.

April 5, 2021

The most practical causal inference book I’ve read (is still a draft)

Causal Inference by Miguel Hernán and Jamie Robins is a must-read for anyone interested in the area.

December 24, 2018

Ask Why! Finding motives, causes, and purpose in data science

Video and summary of a talk I gave at the Data Science Sydney meetup, about going beyond the what & how of predictive modelling.

September 19, 2016

Making Bayesian A/B testing more accessible

A web tool I built to interpret A/B test results in a Bayesian way, including prior specification, visualisations, and decision rules.

June 19, 2016

Diving deeper into causality: Pearl, Kleinberg, Hill, and untested assumptions

Discussing the need for untested assumptions and temporality in causal inference. Mostly based on Samantha Kleinberg’s Causality, Probability, and Time.

May 14, 2016

Why you should stop worrying about deep learning and deepen your understanding of causality instead

Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.

February 14, 2016