Sample Ratio Mismatch (SRM): How to Detect It
Many product decisions in online companies rest on A/B tests. We show one version of the website to one group
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Measuring Loyalty Program Effectiveness Without Bias
A supermarket chain runs a loyalty program across 180 stores. The program has been live for almost two years, and
Incrementality vs Attribution in Marketing
In 2012, eBay switched off its paid brand search ads on Yahoo and MSN and watched what happened to sales.
Confidence Interval vs Credible Interval: An A/B Test Example
Every statistics handbook covers confidence intervals. And everyone who has ever studied statistics has been confused by the fact that
CausalImpact in Python: Measure Campaign Lift
Some of the most important analytics decisions are made without an A/B test. For example, a marketing campaign can be
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Measuring Loyalty Program Effectiveness Without Bias
A supermarket chain runs a loyalty program across 180 stores. The program has been live for almost two years, and
CausalImpact in Python: Measure Campaign Lift
Some of the most important analytics decisions are made without an A/B test. For example, a marketing campaign can be
Regression Discontinuity Design in Python
When Amazon Prime launched in 2005, it changed delivery costs from a transaction cost into a membership benefit, something customers
Causal Inference Made Practical: Unlocking Business Insights with the DoWhy Library
Every analyst has stared at a dashboard showing a seemingly clear pattern, time and again. Premium customers churn more, marketing
Pre/Post Analysis: 3 Reasons Your 38% Uplift Is Fake
The most important question in almost any organisation is also the hardest: Did something work? You launch a new feature, a
Frisch-Waugh-Lovell Theorem: The Engine Behind Causal AI
Marketing teams often celebrate when dashboards show a clear, positive correlation between sales and advertising activity. Applying the Frisch-Waugh-Lovell theorem
Beyond Correlation: A Practical Guide to the Backdoor Criterion in Python
Estimating the effect of one variable on another is central to causal inference. However, observational data can make this challenging,
Parallel Trends Assumption: The Make-or-Break Assumption for Difference-in-Differences
Difference-in-Differences (DiD) is a widely used technique to estimate causal effects when randomised experiments are not feasible. From policy evaluation
Uplift Modelling in Python with Meta-learners
In the world of marketing, deciding which customers to target with specific promotions can feel like a never-ending puzzle — especially when
Propensity Score Matching in Python: Step-by-Step Guide
Evaluating the impact of treatments or interventions is critical in various fields, including business and healthcare. Determining whether a specific