Summary

In this chapter, we discussed regression methods to unearth causality from intricate datasets, beginning with the basics of regression’s role in causality and advancing toward choosing the right models, including both linear and non-linear approaches. We emphasized hands-on application, leveraging R scripts and synthetic datasets to marry theory with practical execution. This journey through various regression techniques such as linear, logistic, and poisson regression equipped you with the necessary skills for model diagnostics and covariate selection, with a special focus on the creation and use of dummy variables to highlight the effects of categorical predictors.

Furthermore, we highlighted the crucial process of orthogonalization in regression analysis, which isolates the unique contributions of treatment variables, independent of confounders. Through practical examples and R code demonstrations, we illustrated the precision of regression models in estimating...