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Testing for Necessary and/or Sufficient Causation: Which Cases Are Relevant?

Published online by Cambridge University Press:  04 January 2017

Jason Seawright
Jason Seawright*
Affiliation:
Department of Political Science, 210 Barrows Hall, University of California, Berkeley, Berkeley, CA 94720-1950. e-mail: seawrigh@socrates.berkeley.edu
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Abstract

Previous researchers have argued that necessary and/or sufficient causes should be tested through research designs that consider only cases with limited combinations of scores on the independent and the dependent variables. I explore the utility for causal inference of the design proposed by these authors, as compared to an “All Cases Design.” I find that, if researchers define the population carefully and appropriately, each case in the population contributes to causal inference and is therefore useful. Previous authors reject this claim on the basis of a view that holds constant the marginal distribution of either the dependent or the independent variable across the working and the alternate hypotheses. I argue that this restriction is not generally appropriate, and hence, an analysis that samples from the entire population is logically defensible. I also argue that this design is more statistically efficient. A reanalysis of two well-known studies demonstrates that sampling from all cases in the relevant population produces greater confidence in the hypothesis than sampling only from cases that experience the outcome.

Type
Articles
Information
Political Analysis , Volume 10 , Issue 2 , Spring 2002 , pp. 178 - 193
Copyright
Copyright © Political Methodology Section of the American Political Science Association 2002 

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