This book has four main parts:

  • Part I introduces causal models and a Bayesian approach to learning about them and drawing inferences from them.

  • Part II applies these tools to strategies that use process tracing, mixed methods, and “model aggregation.”

  • Part III turns to design decisions, exploring strategies for assessing what kind of data is most useful for addressing different kinds of research questions given knowledge to date about a population or a case.

  • In Part IV we put models into question and outline a range of strategies one can use to justify and evaluate causal models.

We have developed an R package—CausalQueries—to accompany this book, hosted on Cran. In addition, a supplementary Guide to Causal Models serves as a guide to the package and provides the code behind many of the models used in this book.