Summary

In this chapter, we provided an in-depth exploration of propensity score techniques in causal inference, highlighting its crucial role in adjusting for confounding variables in observational studies. We began with an introduction to the concept of propensity scores, emphasizing their importance in transforming observational studies so that they resemble randomized trials. Then, we shifted to practical applications using R, where we presented various methods such as matching, stratification, and weighting, each with its unique features and implementation strategies. After, we introduced the theoretical foundations of propensity scores, such as balancing confounding variables and understanding their underlying assumptions and limitations, and also guided you through the practical aspects of estimating and applying these scores using R programming.

Furthermore, we covered different methods of propensity score matching, including nearest-neighbor, caliper, and full matching...