Quick guide

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.

Resources

We (with wonderful colleagues) have developed an R package—CausalQueries—to accompany this book, hosted on Cran. Supplementary Materials, including a guide to the package, can be found at https://integrated-inferences.github.io/.

Corrections

If (when!) we find errors we will correct them using track changes formatting lik htis like this and list notable instances in section 19  Errata.