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This chapter evaluates critical realism, a term which refers to a philosophy of science connected to the broader approach of scientific realism. In contrast to other philosophies of science, such as positivism and post-positivism, critical realism presents an alternative view on the questions of what is ‘real’ and how one can generate scientific knowledge of the ‘real’. How one answers these questions has implications for how one studies science and society. The critical realist answer starts by prioritizing the ontological question over the epistemological one, by asking: What must the world be like for science to be possible? Critical realism holds the key ontological belief of scientific realism that there is a reality which exists independent of our knowledge and experience of it. Critical realists posit that reality is more complex, and made up of more than the directly observable. More specifically, critical realism understands reality as ‘stratified’ and composed of three ontological domains: the empirical, the actual, and the real. Here lies the basis for causation.

This chapter focuses on formal modelling. A formal model is the mathematical exposition of reasoning. Its purpose is to formulate consistent and rigorously stated hypotheses, which often shed light on the causation of a particular social phenomenon. Often, in the social sciences, a formal model is valuable because it can accurately predict behaviour and describe an actual (although unobservable) causal mechanism. Thus, formal models also allow plenty of space for deductive reasoning. Whether they clarify hypotheses or describe a mechanism, the success of formal models remains a matter of debate. The chapter then presents a few examples of useful models and considers the most frequent criticisms of formal modelling in order to identify a series of good practices for its proper use.

This chapter studies causation, which occupies a central place in the social sciences. In their attempts to understand and explain ‘why’ social, economic, and political phenomena occur, scholars have dealt with causality in many different ways. The way to define and observe causal relationships has always been at the heart of harsh academic debates in social as well as natural sciences. Drawing on distinctive ontological and epistemological standpoints, at least four different understandings of causation have emerged in political science. Most authors have adopted a correlational-probabilistic understanding of causation, but some have preferred a configurational one, while others have adopted a mechanistic or even a counterfactual understanding. To illustrate the concrete methodological challenges generated by this theoretical pluralism, the chapter discusses how scholars have dealt with causality to explain the impact of European integration on domestic policies and institutions.

This chapter addresses Boolean algebra, which is based on Boolean logic. In the social sciences, Boolean algebra comes under different labels. It is often used in set-theoretic and qualitative comparative analysis to assess complex causation that leads to particular outcomes involving different combinations of conditions. The basic features of Boolean algebra are the use of binary data, combinatorial logic, and Boolean minimization to reduce the expressions of causal complexity. By calculating the intersection between the final Boolean equation and the hypotheses formulated in Boolean terms, three subsets of causal combinations emerge: hypothesized and empirically confirmed; hypothesized, but not detected within the empirical evidence; and causal configurations found empirically, but not hypothesized. This approach is both holistic and analytic because it examines cases as a whole and in parts.