Bringing Context and Variability Back in to Causal Analysis∗

The methodology of causal analysis in the social sciences is often divided into two ideal type research scenarios: experimental social science and observational social science. For experimental social science, the researcher can manipulate the cause of interest. The most common research design is one where the analyst assigns values of the cause according to a randomization scheme and then calculates post-treatment di¤erences in outcomes across levels of the assigned cause. Typically, the researcher gives little or no attention to individual-speci…c di¤erences in the inferred causal e¤ects or to the context in which the experiment is conducted. 1 For observational social science, the analyst cannot manipulate the cause through intervention because some process outside of the analyst's control determines the pattern of causal exposure. To develop causal assertions, the analyst must adopt a model of causal exposure based on assumptions about how the cause is distributed in the population. Most commonly, a model is adopted that warrants causal inference from di¤erences in outcomes calculated within sets of observed individuals who are exposed to alternative values of the cause but who are deemed otherwise comparable by the maintained model of causal exposure. Individual-level variation in causal e¤ects is then presumed to exist within and across comparison sets, often arising from interactions between individuals'characteristics and the contexts within which they are exposed to the cause. In this chapter, we will discuss methods for modeling causal e¤ects in observational social science, giving particular attention to the capacity of new graphical methods to represent and then motivate models that can e¤ectively deliver estimates 1 This does not mean that the results imply that individual-level di¤erences do not exist. All such di¤erences are, by construction, balanced in expectation across arms of the randomization scheme. Moreover, for the many experiments that are performed on convenience-based collections of individuals willing to participate (e.g., college students), this balancing often does not even generate the average causal e¤ect in a target population of fundamental interest. 1 of underlying heterogeneity of causal e¤ects. We have several related goals that we will pursue in the following order: (1) explain why quantitatively oriented social science that adopted path modeling methodology became a target of critiques that it had ignored variability and context, (2) demonstrate how such e¤ects can be expressed within a more recent methodology of causal graphs, (3) consider feasible empirical strategies to identify these e¤ects, and (4) explain why causal graphs …