Causal analysis with observational data

Causal inference plays a central role in the social sciences. This chapter discusses key questions in causal inquiry: What distinguishes causal questions from descriptive or predictive ones? How can we reason about the assumptions required for causal analysis, and how can we test these assumptions? Using structural causal models and directed acyclic graphs, the chapter explores how to define causal estimands, assess the feasibility of learning from data about them (identification), and evaluate sensitivity to assumption violations. It discusses concrete problems and phenomena such as choosing control variables, post-treatment bias, causal interaction, effect heterogeneity, and mediation. Central issues are exemplified by an analysis of the relationship between exposure to violence and attitudes towards piece among survey respondents in Darfur.