Bayesian data scientist & causal inference specialist
Dr Benjamin T. Vincent is a Bayesian data scientist and causal inference specialist. With Nathaniel Forde, he is writing Bayesian Causal Inference. He is the director of InferenceWorks Ltd, where he offers consulting and workshops to data teams and organisations. He is the lead developer of CausalPy, a Python package for Bayesian causal inference in quasi-experimental settings with 1,100+ GitHub stars. He also created pathmc, a package for Bayesian path analysis and structural causal modeling with a concise formula DSL. He is a contributor to PyMC-Marketing. He works with clients directly through InferenceWorks and collaborates with PyMC Labs, having delivered projects for organisations including HelloFresh, the Bill & Melinda Gates Foundation, and Colgate-Palmolive. He holds a PhD and previously held a faculty position for 15 years applying Bayesian methods to human decision-making research. Originally from Cambridge (UK), he is currently based in Scotland.
Bayesian Causal Inference
Benjamin T. Vincent and Nathaniel Forde
We wrote this book for people who fit models and are then asked what would have happened if the world had been different. It covers causal designs from propensity score weighting to structural equation models. Each is built as a Bayesian model, so its assumptions can be written down, varied and questioned. Causal inference must be robust to stress-tests and sensitivity probes.