Identification: The Key to Credible Causal Inference
Improve your causal IQ and build trust in your causal inference by mastering identification- 24736Murphy ≡ DeepGuide
March Edition: Data and Causality
How data scientists approach causal inference- 25096Murphy ≡ DeepGuide
Unlock the Secrets of Causal Inference with a Master Class in Directed Acyclic Graphs
A step-by-step explanation of Directed Acyclic Graphs from the basics through to more advanced aspects- 27439Murphy ≡ DeepGuide
The Power of Bayesian Causal Inference: A Comparative Analysis of Libraries to Reveal Hidden…
Reveal the hidden causal variables in your data set by using the best-suited Bayesian causal inference library: a comparison with hands-on...- 24836Murphy ≡ DeepGuide
Save your A/B testing by avoiding those 3 costly mistakes
Once exclusively used in academia, in particular medical research, randomized control trials are now a popular method for businesses to make data-driven decisions. In particular, online A/B testing is easy to implement and potentially incredibly powerful- 29979Murphy ≡ DeepGuide
Interview Preparation: Causal Inference
Learn how to tackle interview questions related to causal inference, gaining insights into the core concepts and applications.- 25035Murphy ≡ DeepGuide
The two envelopes problem
How time and causality are emerging from randomness- 20795Murphy ≡ DeepGuide
Read with Me: A Causality Book Club
Starting from a cat story...- 28045Murphy ≡ DeepGuide
Data Tells Us "What" and We Always Seek for "Why"
In my previous article, I kicked off the "Read with Me" book club to explore Judea Pearl’s "The Book of Why". I would like to thank everyone who has shown interest and signed up to join the club. I am hopeful that we can embark o- 24942Murphy ≡ DeepGuide
Building a Custom GPT: Lessons and Tips
From enthusiasm to disappointment and finally the path to solutions and appreciation- 24561Murphy ≡ DeepGuide
Unlock Your Full Potential as a Business Analyst With the Powerful 5-Step Causal Impact Framework
In a business context, the leadership is often interested in the impact of a decision or event on the KPI of interest. As a performance analyst, I spend most of my time answering some variant of this question: "What is the impact of {News, government- 22961Murphy ≡ DeepGuide
Heckman Selection Bias Modeling in Causal Studies
How selection bias is related to the identification assumptions of OLS, and what steps should be taken to address it- 23416Murphy ≡ DeepGuide
Causal Diagram: Confronting the Achilles' Heel in Observational Data
Causal Diagram: Confronting the Achilles’ Heel in Observational Data In my previous two articles, I kicked off the "Read with Me" series and finished reading the first two chapters from "The Book of Why" by Judea Pearl. These art- 28342Murphy ≡ DeepGuide
Why Understanding the Data-Generation Process Is More Important Than the Data Itself
"The Book of Why" Chapters 5&6, a Read with Me series- 23791Murphy ≡ DeepGuide
You Can't Step in the Same River Twice
In my previous articles, we learned about confounders and colliders in observational data that hinder establishing reliable causal relationships. The solution Pearl provided is to draw causal diagrams and use the backdoor criterion to find the sets of con- 23112Murphy ≡ DeepGuide
Philosophy and data science – Thinking deeply about data
Part 3: Causality- 28103Murphy ≡ DeepGuide
What Makes A Strong AI?
"The Book of Why" Chapters 9&10, a Read with Me series- 23676Murphy ≡ DeepGuide
How is Causal Inference Different in Academia and Industry?
A Bonus Article for "The Book of Why" Series- 29055Murphy ≡ DeepGuide
Easy Methods for Causal Inference
Use your favorite models in combination with meta-learners to make valid causal statements- 28431Murphy ≡ DeepGuide
How to Learn Causal Inference on Your Own for Free
The ultimate self-study guide for all levels- 25422Murphy ≡ DeepGuide
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We look at an implementation of the HyperLogLog cardinality estimati
Using clustering algorithms such as K-means is one of the most popul
Level up Your Data Game by Mastering These 4 Skills
Learn how to create an object-oriented approach to compare and evalu
When I was a beginner using Kubernetes, my main concern was getting
Tutorial and theory on how to carry out forecasts with moving averag