Marketing Science

Part 2 | Beyond Attribution: How Causal Inference complements Modern MMM

Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing

August 20, 2025

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8 min read

Let me take you on a journey through how marketing measurement has evolved—and why even our best tools today still miss critical insights.

Sales = β₀ + βtv·TVspend + βsearch·Searchspend + ... + βdisplay·Displayspend + controls + ε

model = LinearRegression() model.fit( X=[tvspend, searchspend, socialspend, seasonality], y=sales )

This approach treats marketing like a vending machine: insert spend, get sales. But it misses everything that makes marketing actually work:

About the author

Cyril Noirot

Lead Data Scientist

Freelance data scientist. I design and ship decision systems — forecasting, pricing, marketing measurement, optimization.

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