Causal Inference and Machine Learning – Deep Learning, NLP, and Beyond
You’ve come a long way! Congratulations!
Chapter 11 marks an important turning point in our journey into causality. With this chapter, we’ll conclude our adventure in the land of causal inference and prepare to venture into the uncharted territory of causal discovery.
Before we move on, let’s take a closer look at what deep learning has to offer in the realm of causal inference.
We’ll start by taking a step back and recalling the mechanics behind two models that we introduced in Chapter 9 – S-Learner and T-Learner.
We’ll explore how flexible deep learning architectures can help us combine the advantages of both models, and we’ll implement some of these architectures using the PyTorch-based CATENets library.
Next, we’ll explore how causality and natural language processing (NLP) intersect, and we’ll learn how to enhance modern Transformer...
