In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences. There is no doubt that the sciences employ a vast array of techniques to address causal questions such as controlled experiments, randomized trials, statistical and econometric tools, causal modeling and thought experiments. But how do these different methods relate to each other and to the causal inquiry at hand? Reiss argues that there is no "gold standard" in settling causal issues against which other methods can be measured. Rather, the various methods of inference tend to be good only relative to certain interpretations of the word "cause", and each interpretation, in turn, helps to address some salient purpose (prediction, explanation or policy analysis) but not others. The main objective of this book is to explore the metaphysical and methodological consequences of this view in the context of numerous cases studies from the natural and social sciences.

Julian Reiss (PhD 2002, LSE) is Professor of Philosophy at Durham University and Co-Director of the Centre for Humanities Engaging Science and Society (CHESS). His main research interests are methodologies of the sciences (especially causality and causal inference, models, simulations and thought experiments, and counterfactuals), philosophy of economics, and science and values.

1. Causation in a Complex World Part I: Evidence 2. What's Wrong with Our Theories of Evidence? 3. Evidence in Context Part II: Singular Causation 4. Counterfactuals, Thought Experiments and Singular Causal Inference in History 5. Counterfactuals in the Social Sciences 6. Contrastive Causation 7. Singular Causation without Counterfactuals Part III: Causal Laws 8. Time Series, Nonsense Correlations, and the Principle of the Common Cause 9. Causal Laws in Biomedical and Social Research: Evidence, Inference, and Purpose Part IV: Semantics 10. Third Time's a Charm: Causation, Science, and Wittgensteinian Pluralism 11. Causation in the Biomedical and Social Sciences: An Inferentialist Account