Portfolio

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

• MMMs and Pearl’s ladder of causal inference
• 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, Colgate-Palmolive, Gain Theory, and HelloFresh. Some of the projects I have worked on have been written up:

• Bayes is slow? Speeding up HelloFresh’s Bayesian AB tests by 60x
• Estimating parameters of a distribution from awkwardly binned data
• Bayesian inference at scale: Running A/B tests with millions of observations
• HelloFresh Media Mix Modelling project
  1. Bayesian Media Mix Modeling for Marketing Optimization
  2. Improving the Speed and Accuracy of Bayesian Media Mix Models
  3. Bayesian Media Mix Models: Modelling changes in marketing effectiveness over time
• Causal sales analytics: Are my sales incremental or cannibalistic?

Talks

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)