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A Simple Explanation of Causal Inference in Python
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 model but that did not work very well.
Next I trawled Google, Medium and Towards Data Science for articles on “causal inference” and I did find a few but they were complicated, often incomplete and not generalizable to my own data sets.
That prompted me to explore the wider literature and the documentation for the various causal libraries that are currently available and this article presents a causal inference model that is the result of that research.
What You Will Learn
By the end of this article you will be able to generate test data to represent any causal inference scenario, build a causal model in Python code and then run “what-if?” queries against the model.
Before we dive into the causal inference model please consider …
Joining Medium with my referral link (I will receive a proportion of the fees if you sign up using this link).
Subscribing to a free e-mail whenever I publish a new story.
Taking a quick look at my previous articles.
Downloading my free strategic data-driven decision making framework.
Visiting my data science website — The Data Blog.
Getting Started
The first thing we need is to import all the libraries needed in the code …
