The fundamental problem of causal inference (FPCI) is one of the most widely-touted methodological tenets in contemporary political science and IR. It is at the core of a wide range of comparative and statistical approaches geared towards accounting for causal relations and it informs how students are introduced to the key task of causal analysis in the study of politics. Our paper takes a different perspective and challenges the oft assumed scope of the problem. We demonstrate that rather than being a problem intrinsically tied to all efforts to make causal inferences, it only relates to a limited subset of counterfactual perspectives on causation and the use of the potential outcomes framework as the guiding principle for making causal inferences in open systems. In particular, processual, interpretive, and generative accounts of causation, along with some counterfactual ones, employed across a variety of approaches in political science do not face the FPCI. Here causal inferences are, for instance, made by tracing the process itself binding a cause and outcome together in some form of productive relationship or by accounting for the configuration of factors that generate a particular outcome. We discuss these assumptions with a view to developing a clearer perspective on the scope of the FPCI and present a range of causal perspectives that are important for knowledge production in the social sciences and from which the problem does not arise.
About the speaker:
Ludvig Norman is Associate Professor (docent) of Political Science at Stockholm University and Senior Fellow at the UC Berkeley Institute of European Studies where he was also Visiting Scholar 2016-2017. He earned his PhD at Uppsala University, held a post-doc position at the Swedish Institute of International Affairs. His research interests include European politics, democratic responses to populism and extremism, the institutions of the European Union and EU democracy. Norman also has a keen interest in social science methodology, and in particular qualitative and mixed-methods approaches to social scientific research.
The Zoom link will be shared upon registration.