A step-by-step guide in understanding the concept of dependence and how to apply it to validate directed acyclic graphs using Python — Introduction Causal Inference is an emergent branch of data science concerned with determining the cause-and-effect relationship between events and outcomes and it has the potential to significantly add to the value that machine learning can generate for organisations.

A step-by-step guide to extending pandas DataFrames with custom methods including full examples of how to implement extensions for conditional probability and expected values — Introduction The Problem Pandas data frames come with a wide range of in-built functions, but there are always new ways that Python programmers will want to interrogate and transform their data. The Opportunity A common approach is to write functions and pass the data frames as parameters but this can become unwieldly, whereas the optimum…

A step-by-step explanation of Directed Acyclic Graphs from the basics through to more advanced aspects — Objective Having spent a lot of time researching causal inference I began to realise that I did not have a full grasp of Directed Acyclic Graphs (DAGs) and that this was hampering my efforts to develop my understanding to a point where I could apply it in order to solve real-world…

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. …

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?”…