Thomas Pinder
Researcher and practitioner in Bayesian causal inference, known for work on framing synthetic control as a Bayesian regression problem and implementing models in PyMC/NumPyro.
Best podcasts with Thomas Pinder
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Apr 2, 2026 • 5min
Bitesize | "What Would Have Happened?" - Bayesian Synthetic Control Explained
Thomas Pinder, researcher in Bayesian causal inference, reframes synthetic control as a Bayesian regression problem. He explains using Dirichlet priors on weights, tuning concentration or placing hyperpriors, and why the Bayesian approach yields richer, less fragile counterfactuals for real-world decision making.
