A full explanation of causal inference front-door adjustment with examples including all the Python source code — Objective By the end of this article you will understand the magic of causal inference front-door adjustment that can calculate the effect of an event on an outcome even where there are other factors affecting both that are unmeasured or even unknown and you will have full access to all the…

Learn to write a simple Python function that will calculate conditional probabilities using notation like p(exam=1 | study=1) — Background As I have continued to delve into causal inference I reached a stage where I needed to be able to construct formulas that use complex combinations of conditional probabilities and the code was starting to be difficult to read and maintain. This led to me developing a simple way to…

A fully working example of the backdoor adjustment formula using Python and the pgmpy library — Introduction In probability theory it is very straightforward to look at a dataset and calculate the probability of an event based on knowing something about other variables. For example:

How to use Python and the DoWhy library to build a causal inference model to explore the causes of global warming — Introduction I published an article recently to provide a simple tutorial of how to get up and running with causal inference using the Pgmpy library - A Simple Explanation of Causal Inference in Python A straight-forward explanation of how to build an end-to-end causal inference model in Pythontowardsdatascience.com

How to master the causal inference do operator and why you need it in your data science tool bag — Introduction Fully explained, end-to-end examples of causal inference that have actual, working source code are very hard to find on the Internet or in books, as I have discovered in my journey to understand how this emerging technology works and why it is so important. But if you persevere it is…

10 Lines of Python code to automate causal discovery that you have got to see — Introduction The focus of my recent research has been causal inference driven by the increasing requests I get from customers to move beyond machine learning predictions to answering “what-if?” type questions to drive impact and outcomes. One of the things that intrigued me initially was — “How are causal diagrams constructed?”…

How to generate intuitive and comprehensive Conditional Probability Tables to visualise and understand causal inference models in 1 line of Python code — Background Causal Inference is a hot topic at the moment but the various libraries that exist can be complicated with inconsistent documentation and examples and most of the available articles and posts focus on a particular aspect of causal inference without covering all the things a data scientist needs to know.

How to generate interactive Directed Acyclic Graphs to visualise and understand causal inference models in 1 line of Python code — Background Causal Inference is very topical at the moment and causal models are starting to become very useful additions to more traditional regression, classification and prediction models. Increasingly customers want to be able to visualise and understand the underlying causes and effects behind model predictions to help answer “Why?”, “What if?”…

A straight-forward explanation of how to build an end-to-end causal inference model in Python — Background I first became interested in causality when I finished a commercial machine learning classification project and the first thing the customers asked after the presentation was … “Why does that happen and what are the underlying causes?” My first attempt revolved around artificially modifying the input data for the classification…