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.
- Read the package release announcement.
- Watch my PyData Global 2022 talk: What-if? Causal reasoning meets Bayesian Inference
Causal inference explainers
- Causal analysis with PyMC: Answering “What If?” with the new do operator
- Interventional distributions and graph mutation with the do-operator
- Regression discontinuity design analysis
- Counterfactual inference: calculating excess deaths due to COVID-19
- What if? Causal inference through counterfactual reasoning in PyMC
- Interrupted time series
- Difference in differences
Didactic/explanatory writing
- Bayesian copula estimation: Describing correlated joint distributions
- Bayesian regression with truncated or censored data
- Truncated regression in Julia/Turing.jl
- Censored regression in Julia/Turing.jl
- Simpson’s paradox and mixed models
- Binomial regression
- Bayesian moderation analysis
- Bayesian mediation analysis
- Piecewise and spline regression in Julia
- Masters-level teaching content on Frequentist and Bayesian methods
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:
Talks
Listed in reverse chronological order.