Summary
This chapter offered you a comprehensive introduction to R programming within the context of causal inference. It began with a foundational understanding of R, including setting up the working environment, basic programming concepts, data types, structures, and functions. The chapter then transitioned into the practical application of causal inference techniques, such as t-tests, regression analysis, and PSM, using various datasets for illustration.
Specifically, the chapter addressed the implementation of PSM analysis in R, with an evaluation of the effect of attending Catholic schools on students’ standardized math scores. The chapter emphasized the importance of statistical techniques in establishing causality and provided guidance on interpreting PSM results. Further depth on PSM and causal analysis in R will be covered in subsequent chapters.
