- Microsoft’s DoWhy is a Cool Framework for Causal Inference - Aug 28, 2020.
Inspired by Judea Pearl’s do-calculus for causal inference, the open source framework provides a programmatic interface for popular causal inference methods.
- Forecasting Stories 4: Time-series too, Causal too - Jun 1, 2020.
This article is about the story of taking effective business decisions basis a combined model. Let us together study how these components work hand in hand.
- Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown - Jan 19, 2019.
Why are soda and ice cream each linked to violence? This article delivers the final word on what people mean by "correlation does not imply causation."
- Causation in a Nutshell - Jul 20, 2018.
Every move we make, every breath we take, and every heartbeat is an effect that is caused. Even apparent randomness may just be something we cannot explain.
- KDnuggets™ News 18:n24, Jun 20: Data Lakes – The evolution of data processing; Text Generation with RNNs in 4 Lines of Code - Jun 20, 2018.
How to spot a beginner Data Scientist; How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning; Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray; Cartoon: FIFA World Cup Football and Machine Learning
- The Book of Why - Jun 1, 2018.
Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.
- THE BOOK OF WHY: The New Science of Cause and Effect - May 15, 2018.
A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize AI.
- 4 Common Data Fallacies That You Need To Know - Dec 5, 2017.
In this post you will find a list of common the data fallacies that lead to incorrect conclusions and poor decision-making using data. Here you will find great resources and information so that you can always be reminded of these fallacies when you’re working with data.