Abstract
The paper raises difficulties for causation and causal inferences in social research, including counterfactuals, transitivity, inference in probabilistic causation, and manipulating and controlling variables. It suggests limits to causal modelling in understanding causality and causal processes in social research. Problems with randomization in social research are raised. Causal modelling, premised on a simplistic and linear view of causation, sits uncomfortably with a social world that is complex and non-linear and in which causal processes replace input–output models. The paper suggests that complexity theory opens up new approaches to causality, regarding the social world holistically, with multi-directional causality and feedback in unpredictable contexts, and with webs and networks of non-linear causality rather than causal lines, chains and simple linearity. The paper identifies and exemplifies approaches to understanding and researching causation in complexity-theory-based social research.
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