# Preface

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.

• Everything up to Part IV assumes that we have access to models we are happy with. In Part IV we turn to the difficult question of model justification and outline a range of strategies on can use to justify 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.