Employing Regression Approaches for Causal Inference

We have learned a lot till now. This chapter will further lead you into the deep roots of regression-based methods to discern causality. We will keep our pace pretty much the same as other chapters, which means we will first start with theory and then get to practice the learned theoretical models in real use cases in R, using provided coding scripts and synthetic datasets.

Choosing the right model is an art as much as it is a science, influenced by the nature of the data at hand and the specific causal relationships under investigation. In this chapter, we go deep into model selection, providing you with the insights needed to make informed decisions in choosing the best model for the job. We’ll tackle model diagnostics to assess and address the assumptions that underpin the models you deploy. In this, we will discover a wide range of regression models, spanning linear and non-linear versions. We will learn about the assumptions...