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Most marketing mix models are tuned until the numbers flatter the brief. Here's the case for pre-specifying the model instead, and how mmm-framework builds the discipline into its API.

Controlling for a variable to identify your treatment effect doesn't mean that variable's coefficient is causally interpretable — and confusing the two is surprisingly common.

Welcome to my corner of the internet. A quick note on what this blog is for.

A fitted model predicts response; it doesn't hand you the best budget. Atlas is a model-agnostic framework that turns any predictive model into constraint-respecting spend recommendations.

Treating a Bayesian marketing-mix model as declarative configuration — variables, transforms, normalization, and priors in JSON — so a malformed input file fails validation before you fit instead of after you've shipped the wrong number.

Why weekly brand-tracker survey data needs a Binomial likelihood instead of raw percentages, what a small PyMC model actually buys you, and where partial pooling and state-space smoothing come next.