Choosing the appropriate regression model
As you will see in this chapter, there are a plethora of regression models (many available in R). Selecting the appropriate regression model for causal inference is a critical decision that significantly influences the accuracy of causal effect estimates, the clarity of results interpretation, and the robustness of analytical conclusions. This process demands a thoughtful assessment of various factors, including the type of outcome variable, the relationship dynamics between covariates, adherence to model assumptions, and the need to account for confounding and interaction effects (we will look at this in a bit). Through a systematic review of these elements, we will choose a regression model that not only aligns with your data and business objectives but also strengthens the validity and reliability of the causal findings. In this section, let’s familiarize ourselves with what it entails to deploy a regression model.
