This chapter shows how to use causal models to inform the selection of cases for intensive analysis. We outline a procedure in which we predict the inferences that will be made when future data are found and use these predictions to inform case-selection strategies. We ask: Given a set of cases on which we already have data on X and Y, which cases will it be most advantageous to choose for more in-depth investigation? We show that the optimal case-selection strategy depends jointly on the model we start with and the causal question we seek to answer and draw out the implication that researchers should be wary of generic case-selection principles.

This chapter addresses the question of whether causal models can themselves be justified. We outline strategies for justifying models on the basis of prior data and so empirically grounding beliefs about the probative value of clues.

In this concluding chapter, we summarize the key payoffs of the causal-model-based approach to causal inference, point to a set of important limitations of the approach, and sketch out what we see as next steps in strengthening model-based inference.

We describe strategies for figuring out whether a model is likely doing more harm than good and for comparing the performance of different models to one another.

We provide a lay-language primer on the counterfactual model of causal inference and the logic of causal models. Topics include the representation of causal models with causal graphs and using causal graphs to read off relations of conditional independence among variables in a causal domain.

This chapter illustrates how we can express theoretical ideas in the form of a causal model by translating three arguments from published social science research into models. We illustrate using Paul Pierson’s (1994) work on welfare-state retrenchment, Elizabeth Saunders’ (2011) research on military intervention strategies, and Adam Przeworski and Fernando Limongi’s (1997) study of the relationship between national wealth and democracy.

This chapter extends the analysis from Chapter 7 to multi-case settings and demonstrate how we can use the approach to undertake mixed-method analysis. We show how, when analyzing multiple cases, we can update our theory from the evidence and then use our updated theory to draw both population- and case-level inferences. While single-case process tracing is entirely theory-informed, mixed-data inference is thus also “data”-informed. We show how the approach can integrate information across any arbitrary mix of data structures, such as “thin” data on causes and outcomes in many cases and “thicker” process evidence on a subset of those cases.

We connect the literature on causal models to qualitative inference strategies used in process tracing. The chapter outlines a procedure for drawing case-level causal inferences from a causal model and within-case evidence. We also show how a key result from the causal-models literature provides a condition for when the observation of a node in a causal model (a “clue”) may be (or certainly will not be) informative, and we extract a set of implications for process-tracing methods.

We apply the causal-model-based approach to process tracing to two major substantive issues in comparative politics: the relationship between inequality and democratization and the relationship between institutions and growth. Drawing on case-level data, we use qualitative restrictions on causal types together with flat priors to draw inferences about a range of causal queries. The applications illustrate the different types of learning that can be gleaned from information on moderators and mediators, as well as the scope for learning from historical data when researchers have informative beliefs about confounding processes.