The examples are drawn from a wider range of the social and behavioral sciences than is usually the case — demography, public health, and migration, for example. Her insight is that we can learn a great deal about social science research by looking at good examples of empirical and theoretical reasoning about social changes.
- Measuring Variations?
- Measuring Causal Effects in the Social Sciences!
- The Nature of Causal Effects and How to Measure Them.
- Computer Science Logic: 11th International Workshop, CSL 97 Annual Conference of the EACSL Aarhus, Denmark, August 23–29, 1997 Selected Papers.
- Causality and Causal Modelling in the Social Sciences Buch.
- Causality and causal modelling in the social sciences : measuring variations - Semantic Scholar;
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I think this is exactly right: it is much better to discover the problems that percolate out of the practice of social inquiry rather than imposing a framework of philosophical expectations onto social science practice. Russo focuses her attention on the problem of explaining variation: what causes the variation of a certain characteristic over a population of individuals or events. She offers a very adept discussion and interpretation of the meaning of causal statements and causal reasoning in the social sciences.
The book provides a rigorous contribution to the large literature on the logic of quantitative reasoning about causes of population characteristics. Her case studies are well selected and well done. The book is founded on a deep and rigorous understanding of the most recent philosophical and methodological work on causal modeling. The author does a very good job of positioning her understanding of the meaning of causal modeling and causal judgments in the social sciences.
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Her chapter on causal mechanisms is a significant contribution to this growing debate within the philosophy of social science; she correctly observes that we need to have greater precision in our discussion of what a causal mechanism is supposed to be. The book will be of substantial interest to the social scientists, psychologists, demographers, and philosophers who are interested in current debates about the mathematics and philosophy of causal inference.
Social scientists and philosophers such as Skyrms, Cartwright, Woodward, Pearl, and Lieberson have developed a very deep set of controversies and debates about the proper interpretation of causal inference. You are commenting using your WordPress.
You are commenting using your Google account. You are commenting using your Twitter account. You are commenting using your Facebook account. Notify me of new comments via email. Notify me of new posts via email. Hartmann, M. Stoeltzner, M.
Measuring variation : an epistemological account of causality and causal modelling/
Weber eds , Probabilities, Laws, and Structures , Springer. Correlational data, causal hypotheses, and validity. Journal for General Philosophy of Science.
In press. Critical Reflections.
Csf Russo Measuring Variations
Williamson Models for prediction, explanation, and control: recursive Bayesian Networks. Theoria , 26 70 , Inferring causality through counterfactuals in observational studies. Some epistemological issues.
A Bayesian formalization of the precautionary principle in pharmaceutical policy. Federica has joined the editorial board of Philosophy and Technology and of Topoi.