Reviewing the first three chapters of the book Causal Machine Learning by Robert Osazuwa Ness.
Epidemiologists analyse clinical trials to estimate the intention-to-treat and per-protocol effects. This post applies their strategies to online experiments.
Back-dated meta-post that gathers my posts on Automattic blogs into a summary of the work I’ve done with the company.
Sharing remote teamwork insights, my climate & sustainability activism, Reef Life Survey publications, and progress on Automattic’s Experimentation Platform.
Causal Inference by Miguel Hernán and Jamie Robins is a must-read for anyone interested in the area.
Video and summary of a talk I gave at the Data Science Sydney meetup, about going beyond the what & how of predictive modelling.
A web tool I built to interpret A/B test results in a Bayesian way, including prior specification, visualisations, and decision rules.
Discussing the need for untested assumptions and temporality in causal inference. Mostly based on Samantha Kleinberg’s Causality, Probability, and Time.
Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.