Glossary · Marketing Engineering
Causal Inference
also: causal graph · DAG · directed acyclic graph
Definition
Causal inference is the statistical machinery for estimating causal effects from data rather than just describing correlations. In marketing it involves directed acyclic graphs (DAGs) for identifying confounders, instrumental variables for unobserved confounding, and quasi-experimental methods like difference-in-differences and synthetic control.
Pearl's causal framework distinguishes three rungs: (1) association (P(Y|X)), (2) intervention (P(Y|do(X))), and (3) counterfactuals. Marketing measurement typically needs rung 2. DAGs let practitioners reason about which variables must be controlled (back-door criterion) and which should not be (colliders). Methods include propensity score matching, instrumental variables, difference-in-differences, synthetic control, and Bayesian structural time series (CausalImpact).
Essays on this concept
- Business Analytics
Causal Discovery in Business Data: Applying PC Algorithm and FCI to Find Revenue Drivers Without Experiments
Correlation tells you that feature usage and retention move together. It doesn't tell you which causes which — or whether a third factor drives both. Causal discovery algorithms can untangle this from observational data alone.
- Marketing Engineering
Multi-Touch Attribution Is Broken — A Causal Inference Approach Using Directed Acyclic Graphs
MTA models overestimate retargeting by 340% and underestimate display by 62%. The fix isn't better heuristics — it's abandoning correlational attribution entirely in favor of causal graphs.
- Marketing Engineering
Causal Impact of SEO on Branded Search: A Synthetic Control Method for Organic Channel Measurement
SEO is the only major marketing channel where practitioners still argue about whether measurement is even possible. Synthetic control methods borrowed from policy economics prove it is — and the results will surprise you.
- Business Analytics
The Analytics Engineering Manifesto: Why dbt Changed the Data Team Operating Model Forever
Before dbt, analysts wrote SQL that nobody reviewed, nobody tested, and nobody documented. The tool was simple — SQL templating with version control. The impact was structural: it created an entirely new discipline.
- Digital Economics
Attention Economics Quantified: Measuring the True CPM of Cognitive Load in Digital Advertising
CPM measures whether an ad loaded in a browser. It says nothing about whether a human noticed it. Here's a framework for pricing what actually matters — the cognitive cost of attention — and why the gap between CPM and true attention cost is where billions in ad spend disappear.
- Business Analytics
Bayesian A/B Testing in Practice: When to Stop Experiments and How to Communicate Results to Non-Technical Stakeholders
Frequentist A/B testing answers a question nobody asked: 'If the null hypothesis were true, how surprising is this data?' Bayesian testing answers the question that matters: 'Given this data, what's the probability that B is actually better?'
- Behavioral Economics
Choice Architecture at Scale: How Default Options Drive $2.3B in Incremental E-commerce Revenue
An empirical examination of default effects in digital commerce — from Thaler and Sunstein's nudge theory to the precise mechanics of how pre-selected options generate billions in revenue most consumers never consciously chose to spend.
- Marketing Engineering
Incrementality Testing at Scale: A Geo-Lift Framework for Measuring True Campaign Impact
Half your marketing budget is wasted. The classic joke, except now we can identify which half — geo-lift experiments measure what would have happened without the campaign, not just what happened with it.
- Marketing Engineering
Marketing Mix Modeling in the Privacy-First Era: Bayesian Structural Time Series Without User-Level Data
Cookies are dying. Deterministic attribution is shrinking. The irony: the measurement approach from the 1960s — Marketing Mix Modeling — is making a comeback, now powered by Bayesian inference that would have been computationally impossible when it was first invented.
- E-commerce ML
Personalized Promotion Optimization: Uplift Modeling to Identify Who Needs a Discount vs. Who Would Buy Anyway
70% of promotional spend goes to customers who would have purchased at full price. Uplift modeling identifies the 30% whose behavior actually changes with a discount — and ignores the rest. The math isn't complicated. The organizational willingness to stop blanket discounting is.
- E-commerce ML
Search Ranking as a Revenue Optimization Problem: Learning-to-Rank with Business Objective Regularization
E-commerce search is not Google search. When a user types 'running shoes,' the goal isn't to find the most relevant document — it's to surface the product most likely to be purchased at the highest margin. This reframes ranking as a constrained revenue optimization problem.
- Behavioral Economics
Temporal Construal Theory Applied to Landing Pages: Abstract vs. Concrete Messaging by Funnel Stage
Your top-of-funnel landing page should sell the dream. Your bottom-of-funnel page should sell the mechanism. Construal Level Theory explains why — and the data shows a 34% conversion gap when you get this wrong.
- Marketing Engineering
Unified Measurement Architecture: Connecting MMM, MTA, and Experimentation Into a Single Source of Truth
MMM says Facebook works. MTA says Google works. The incrementality test says neither works as well as you thought. Three measurement systems, three different answers — here's how to reconcile them into one coherent picture.
Authoritative references