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
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.