Measuring the Causal Effect of Free Shipping with Regression Discontinuity Design
When Amazon Prime launched in 2005, it changed delivery costs from a transaction cost into a membership benefit, something customers
Blog Name
A short description introducing your blog so visitors know what type of posts they will find here.
Where European Giants Fall: The Math Behind Bodø/Glimt’s Champions League Run
On 20 January 2026, Manchester City arrived at Aspmyra Stadion in Bodo for a Champions League group stage game. They
Women’s Education vs. Fertility Rate: A Global Pattern
Women’s education is one of the strongest predictors of fertility decline globally — 157 countries, r = -0.79. The pattern
Join our newsletter to stay updated
Measuring the Causal Effect of Free Shipping with Regression Discontinuity Design
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 stared at a dashboard showing a seemingly clear pattern many times. Premium customers churn more, marketing campaigns decreased
Did It Really Work? The Hidden Dangers of Pre/Post Analysis
The most important question in almost any organisation is also the most difficult one: Did something work? You launch a new
The Simple Theorem Behind Modern Causal AI
Marketing teams often celebrate when dashboards show a clear, positive correlation between sales and advertising activity. Our campaigns are driving
Beyond Correlation: A Practical Guide to the Backdoor Criterion in Python
Estimating the effect of one variable on another is at the core of causal inference. However, doing this with observational
Parallel Trends: The Make-or-Break Assumption for Difference-in-Differences
Difference-in-Differences (DiD) is a widely used technique to estimate causal effects when randomized experiments are not feasible. From policy evaluation
Causal AI in Action: Drive Conversions with Uplift Modeling
In the world of marketing, deciding which customers to target with specific promotions can feel like a never-ending puzzle — especially when
Causal Inference with Python: An Ultimate Guide to Propensity Score Matching
Evaluating the impact of treatments or interventions is critical in various fields, including business and healthcare. Determining whether a specific
Mastering Causal Inference with Python: A Guide to Synthetic Control Groups
One can feel intrigued when a newspaper like the Washington Post writes an article about the statistical method. Statistical modeling