Causality and Causal Modelling in the Social Sciences: Measuring Variations: 5 (Methodos Series) - Softcover

From the reviews:

"Few books in philosophy specifically deal with causality in the social sciences. Fewer still draw on real examples taken from the social science literature. Here is an exception to the rule. Though a philosopher, Dr. Russo has an extensive background in the social sciences and has collaborated with social scientists in several of her research projects.

Causality and Causal Modelling in the Social Sciences: Measuring Variations is therefore a compulsory reading both for philosophers of science and for social scientists. For philosophers on the one hand, reading this book is a good way of experiencing how social science is actually done, far from the arm-chair view of scientific practice. For social scientists on the other hand, it is a way of confronting their practice in causal research to broader concerns in the philosophy of science.

Strongly recommended also to the sceptics who believe that, because there are no general laws in the social sphere, causal explanations are impossible. They will think twice after reading this book."

(Prof. Guillaume Wunsch, Institute of Demography, University of Louvain, Belgium)

“In Causality and Causal Modelling in the Social Sciences, Federica Russo attempts a mutually enlightening exchange between the philosophical literature on causation and causal modeling approaches in social science. ... The coverage of the philosophical literature on causation is good ... . does a good job of providing an overview of current philosophical discussions of causation and probability, and I think it should be commended for attempting to systematically engage this philosophical literature with real social science research.” (Daniel Steel, British Journal for the Philosophy of Science, Vol. 63 (3), September, 2012)

This investigation into causal modelling presents the rationale of causality; i.e. what guides reasoning in causal modeling. In contrast to the dominant paradigm, it argues that causal models are governed by a variation, rather than regularity or invariance.