Introduction to the Virtual Issue: Past and Future Research Agenda on Causal Inference

Abstract

If you ask political scientists whether a major goal of their empirical research is to test causal relationships, I suspect that most of them answer “yes.” In contrast, many statisticians would say that their methods may improve predictions but have little to do with causal inference. As a result, even as an increasing number of social scientists use statistical methods to infer causal effects in their empirical research, many of these methods were not originally designed for causal inference. This intellectual gap has been rapidly filled by two “revolutions.” In the community of empirical researchers, the identification revolution has occurred where the key assumptions for identifying causal quantities are taken much more seriously than before. Problems related to measurement and selection bias are no longer brushed under the table. The discipline has recently witnessed a rise in the use of randomized experiments, and an increasing number of political scientists now choose research designs to improve the credibility of identification assumptions (for example, see the 2009 Political Analysis special issue on “Natural Experiments in Political Science”). Simultaneously, among methodologists in various disciplines, the potential outcomes revolution has taken place where traditional statistical methods are reframed within the formal framework of causal inference. The potential outcomes framework, which originates in the classic works of Neyman and Rubin, makes explicit the fundamental problem of causal inference: to infer causal effects one must estimate counterfactual outcomes from observed data. Along with the development of new methodologies, this revolution has led to re-interpretations of familiar statistical methods such as regression and instrumental variables methods, and clarified the exact conditions under which they can be used to infer causal relationships from observed data. Many political methodologists contributed to these new developments. Here, I selected six articles published in Political Analysis between 2005 and 2010 that represent the leadership taken by political methodologists to contribute to advancement of causal inference. These articles have influenced the way in which political scientists and other social scientists conduct their empirical research, and three of them won the Warren Miller best paper award for 2005, 2007, and 2009. After briefly discussing each article with references to other relevant Political Analysis articles, I identify further research challenges where unresolved and yet important methodological problems await the contributions from political methodologists.