To get the most out of this book

To fully benefit from this book, it is recommended you have some understanding of basic statistical concepts, including distributions, hypothesis testing, and confidence intervals. Familiarity with fundamental data analysis techniques, such as linear and logistic regression, may help further (but are not necessary), as these will provide a basis for understanding the more advanced causal inference methods covered in this book.

In terms of programming, having some experience with R is highly advantageous but not essential. You should be comfortable with basic R operations, such as data manipulation using data.frame, handling different data types, and using common functions and packages such as dplyr and ggplot2 for data analysis and visualization. If you are new to R, don’t worry – the book includes introductory sections on setting up R and guiding you through essential R skills. However, having a basic programming mindset will help you grasp more complex coding tasks.

Regarding system requirements, you will need a laptop or desktop computer capable of running R and RStudio Desktop, the popular integrated development environment (IDE) for R. A computer with at least 8 GB of RAM is recommended to efficiently handle larger datasets and perform computations required for causal analysis. While R itself is not resource-intensive, some of the analyses and models discussed in the book, especially those involving large datasets or complex simulations, will benefit from a modern multi-core processor and ample storage space. Additionally, having a stable internet connection will be useful for downloading R packages and accessing online resources.

Setting up the coding platform R Studio is detailed step by step in Chapter 3.

Software/hardware covered in the book	Operating system requirements
RStudio Desktop	Windows, macOS, or Linux
R

Important note on sensitivity and analytical focus of the practical examples

The practical examples covered in this book may explore complex societal issues, however, our focus is solely on learning about the analytical methods. The insights gained from the examples are intended to demonstrate statistical and computational techniques, not to provide definitive conclusions about real-world problems.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.