Causal Inference
22 articles on causal inference
Can Your Brand Measure Black Friday Lift at All? The Floor
Before spending a cent on a Black Friday test, two tables and one multiplication say whether it can produce an answer. For a typical DTC brand the standard design sees an 8.3% lift and nothing smaller. Find your row.
A Dutch Province Geo Test Cannot Reach p Below 0.05
One treated province against eleven placebos gives twelve possible orderings, so the best p-value the design can return is 1 in 12. That is above 0.05 before a euro is spent. The floor by country, and the three fixes.
Retargeting Holdout Before Black Friday: The Mystery Box
Retargeting reaches people who already came to your store, which is why it reports well and why nobody knows what it adds. A regional holdout before Black Friday is the only way to find out, and the three possible answers are all useful.
Ask Your Attribution Vendor for a Placebo Test
Thirty-one commercial measurement vendors, audited between 14 August and 2 September 2026, and not one published validation against randomised experiments. Four questions to ask yours before the Black Friday read, including the one about nothing.
Point AI at the Wrong Question and It Will Confidently Lie
A model will answer whatever you ask with total confidence. The trick is asking a causal question, not a correlation one.
How to Prove a Channel Caused Revenue Without Running an Experiment
Geo holdouts are not the only way to prove incrementality. Here is how causal inference on the GA4 export you already own answers the same question in minutes, and when you should still run the experiment.
Is GA4's Data-Driven Attribution a Black Box You Can Trust?
GA4's data-driven attribution is a real model, but it is correlational, unauditable, and many smaller properties are not even running it. Here is how to check yours, and what to do instead.
Structural Equation Modeling for Attribution: Mapping the Full Causal Chain
Structural Equation Modeling (SEM) replaces broken attribution with causal inference. See why SEM attribution beats LLMs (GPT-4o: 10.1% SQL accuracy) and maps full causality chains.
Difference-in-Differences for Marketing: Measuring Campaign Impact Scientifically
Difference-in-differences (DiD) cuts through marketing noise with causal inference. Learn why 964 brands use DiD to measure true campaign impact vs. flawed attribution models.
Synthetic Control Methods for Marketing: Building Your Counterfactual
Synthetic control methods cut through the noise of broken attribution.industry’s 30-60%.
How LLMs Mishandle NULL Values in Marketing Data
LLMs struggle with missing marketing data. NULL values cause inaccurate attribution. Causality Engine uses causal inference for robust behavioral intelligence, sidestepping LLM limitations.
Granger Causality in Marketing: Does Your Ad Spend Actually Cause Revenue?
Granger causality in marketing claims to prove ad spend causes revenue—but does it? Spoiler: No. We break why time series causality fails and what actually works.
Association vs. Causation in Marketing: The Expensive Mistake
Stop wasting your marketing budget. Learn the critical difference between association vs causation and how it impacts your ROI.
What Is Causal Inference and Why Every Marketer Needs It
Stop guessing and start knowing. Learn what causal inference is and how it reveals the true impact of your marketing spend.
The Frontdoor Criterion Explained for Marketing Analytics
Learn how the Frontdoor Criterion, a powerful causal inference method, can fix your broken marketing analytics and reveal true campaign impact.
The Causality Chain: How One TikTok Ad Creates a Meta Conversion 21 Days Later
Uncover the hidden causality chain in your marketing. Learn how a single TikTok ad can lead to a Meta conversion weeks later, and why traditional attribution misses it.
Counterfactual Analysis for Ad Spend: What Would Have Happened Without That Campaign
Discover how counterfactual analysis in marketing reveals the true impact of your ad spend. Stop guessing and start measuring real incrementality.
Directed Acyclic Graphs for Marketing: A Practical Guide
Stop guessing and start knowing. This practical guide to Directed Acyclic Graphs (DAGs) for marketing shows you how to build causal models that reveal true ROI.
Causal AI in Marketing: From Correlation to Confidence
Stop guessing and start knowing. Learn how Causal AI is revolutionizing marketing by moving beyond simple correlation to deliver true causal insights.
Instrumental Variables in Marketing: Isolating True Channel Impact
Stop guessing your channel impact. Learn how instrumental variables in marketing isolate the true causal effect of your campaigns, moving beyond flawed attribution.
How Causal Inference Reveals Which Channels Actually Drive Sales
Stop guessing which channels drive sales. Causal inference provides the ground truth on channel performance for Dutch ecommerce brands.
Why Correlation-Based Marketing Decisions Cost You 30% of Your Budget
Stop wasting your marketing budget. Learn why correlation-based marketing decisions are costing your brand and how causal inference can reveal true ROI.
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