Did AI anxiety push people toward trade careers?
Search interest in “how to become a plumber” jumped after ChatGPT launched in late 2022. And has stayed elevated ever
Blog Name
A short description introducing your blog so visitors know what type of posts they will find here.
FDI Inflows to Africa
Africa’s FDI inflows have increased every decade since 1970.Near zero then. $57B now. The continent needs this to continue. Investment
Radar Charts: Why They Mislead More Than They Reveal
Radar charts are a very popular visualisation tool. Recently, Anthropic released one which went viral and was used to visualise
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
Join our newsletter to stay updated
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