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.
Causal Inference
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.
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.
Some highlights from 2020
Sharing remote teamwork insights, my climate & sustainability activism, Reef Life Survey publications, and progress on Automattic’s Experimentation Platform.
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.
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.
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.
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.
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.