Summary
In this chapter, you were introduced to DR estimation, a method that cleverly balances two models – the exposure/treatment model and the outcome model – to ensure our causal inference is on solid ground, even if one of the models decides to go off track. This chapter also looked through R code so that we weren’t just learning but also on an electrifying journey to uncover the truth behind the data. It was all about getting our hands dirty with R, making sense of complex datasets, and ensuring our analysis remained robust, regardless of our challenges.
We walked through the mathematics and practical applications of the DR estimation method, employing R to bring theoretical concepts to life. This discussion transitioned from the basics to more complex techniques, emphasizing the method’s flexibility and resilience. At this point, we’re not just acquainted with DR estimation; we’re empowered to apply it confidently in our analyses....
