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date: 25 May 2022

Abstract and Keywords

This article describes the methods for modeling causal effects in observational social science. It considers the capacity of new graphical methods to represent and then motivates models that can effectively deliver estimates of the underlying heterogeneity of causal effects. The major advancements that have allowed scholarship to move beyond simple regression models are also elaborated. There are two basic goals of writing down a causal graph: (1) to present the set of causal relationships implied by the available state of knowledge, (2) to evaluate the feasibility of alternative estimation strategies. Causal graphs can obscure important distinctions precisely due to their flexibility. Thus, their flexibility enables careful and precise consideration of the challenges of causal effect identification, separated in helpful ways from many specification issues that are less fundamental.

Keywords: causal effects, social science, heterogeneity, modeling, causal graphs

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