Posts
All the articles I've posted.
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NO TEARS: How Acyclicity Became Differentiable
Learning the structure of a causal graph from data used to be a combinatorial search over a space that grows superexponentially. One change of variables turned it into gradient descent.
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The Illusion of Significance: Why p-value Variable Selection Breaks Marketing Mix Models
Stepwise regression produces overconfident models with biased estimates — here's why that matters for MMM, and how Bayesian shrinkage priors fix the problem without breaking your causal identification.
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Noisy Covariates Bias Your Coefficients Toward Zero
When a predictor in your regression is measured with error, its coefficient shrinks toward zero in a predictable, quantifiable way — here's the math and the Bayesian fix.
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Building a Pre-Specified Bayesian MMM
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.