Amaljith - Amaljith

Causal Machine Learning - Part 2

This post is the second post of the series on Causal Machine Learning. This blog post is based on the work of Judea Pearl. I will discuss how naive statistics can fail (Spurious Correlations , Simpson Paradox & Asymmetry In Causal Inference).

Machine Learning

Introduction to Causality In Machine Learning - Part 1

This post is the first of the series on Causal Machine Learning. I will start with the very basics of causal inference in this. Enjoy the reading!

Explainable AI

Shapley Additive Explanations (SHAP)

Shapley Additive Explanations (SHAP) is a game-theoretic approach to interpret the output of any machine learning model. It aims to explain the contribution of each feature to the final prediction of the model, thereby providing a clear understanding of how the model makes its decisions.

Explainable AI

Explaining Bayesian Neural Networks

Explainable AI refers to models that can easily be interpreted by humans. Bayesian inferencing is a natural choice to obtain explanations of trained deep learning models.

Machine Learning

Causal Machine Learning - Part 5

This post is the fifth post of the series on Causal Machine Learning. This blog post is based on the work of Judea Pearl. As always i will try to keep the things as simple as possible. So stay with me , Enjoy reading !

Machine Learning

Causal Machine Learning - Part 4

This post is the fourth post of the series on Causal Machine Learning. This blog post is based on the work of Judea Pearl. As always i will try to keep the things as simple as possible. So stay with me , Enjoy reading !

Machine Learning

Causal Machine Learning - Part 3

This post is the third post of the series on Causal Machine Learning. This blog post is based on the work of Judea Pearl. In this blog post i have discussed Pearl’s three-level causal hierarchy , How to represent Causality and little bit about Causal Inferences & Causal Discovery.