I have written extensively on Bayesian analysis methods. Below are links to didactic notes, official PyMC examples, blogs posts at PyMC Labs as well as write-ups of (some) of the client work I have been involved in.

The CausalPy package

Late in 2022, we released CausalPy, which I developed in conjunction with PyMC Labs. CausalPy is a Python package focussing on causal inference in quasi-experimental settings. The package allows users to choose between Bayesian (as well as traditional OLS) model estimation methods to be used.

Causal inference explainers

Didactic/explanatory writing

Write-ups of client projects

I spend much of my time consulting with PyMC Labs. Some of the clients I have worked with include: Alva Labs, the Bill & Melinder Gates Foundation, Gain Theory, and HelloFresh. Some of the projects I have worked on have been written up:


Listed in reverse chronological order.

Bayesian causal inference: why you should be excited (BP Causal Inference Symposium, 2023)

Interview on Learning Bayesian Statistics podcast, 2023

What-if? Causal reasoning meets Bayesian Inference (PyData Global 2022)