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From association to causation: Some remarks on the history of statistics
 Statist. Sci
, 1999
"... The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More ..."
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Cited by 30 (7 self)
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The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More recently, investigators in the social and life sciences have used statistical models and significance tests to deduce causeandeffect relationships from patterns of association; an early example is Yule’s study on the causes of poverty (1899). In my view, this modeling enterprise has not been successful. Investigators tend to neglect the difficulties in establishing causal relations, and the mathematical complexities obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Snow’s work on cholera will be contrasted with modern studies that depend on statistical models and tests of significance. The examples may help to clarify the limits of current statistical techniques for making causal inferences from patterns of association. 1.
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs c ..."
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This paper (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Statistical Models for Causation: What Inferential Leverage Do They Provide
 Evaluation Review
, 2006
"... The online version of this article can be found at: ..."
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Cited by 18 (4 self)
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The online version of this article can be found at:
Causation, Statistics, and Sociology
 EUROPEAN SOCIOLOGICAL REVIEW,VOL. 17 NO. 1, 1^20
, 2001
"... Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as ..."
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Three different understandings of causation, each importantly shaped by the work of statisticians, are examined from the point of view of their value to sociologists: causation as robust dependence, causation as consequential manipulation, and causation as generative process. The last is favoured as the basis for causal analysis in sociology. It allows the respective roles of statistics and theory to be clarified and is appropriate to sociology as a largely nonexperimental social science in which the concept of action is central.
Toward improved use of regression in macrocomparative analysis
 Comparative Social Research
, 2007
"... I agree with much of what Michael Shalev (2007) says in his paper, both about the limits of multiple regression and about how to improve quantitative analysis in macrocomparative research. With respect to the latter, Shalev suggests three avenues for advance: (1) improve regression through technica ..."
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Cited by 8 (3 self)
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I agree with much of what Michael Shalev (2007) says in his paper, both about the limits of multiple regression and about how to improve quantitative analysis in macrocomparative research. With respect to the latter, Shalev suggests three avenues for advance: (1) improve regression through technical refinement; (2) combine regression with case studies (triangulation); (3) turn to alternative methods of quantitative analysis such as multivariate tables and graphs or factor analysis (substitution). I want to suggest some additional ways in which the use of regression in macrocomparative analysis could be improved. None involves technical refinement. Instead, most have to do with relatively basic aspects of quantitative analysis that seem, in my view, to be commonly ignored or overlooked. LOOK AT THE DATA Shalev’s third suggested path for progress consists of using tables, graphs, and tree diagrams to examine causal hierarchy and complexity and to identify cases meriting more indepth scrutiny. This should be viewed not as (or at least not solely as) a substitute for regression but rather as a critical component of regression analysis. All of us were (I hope) taught in our first
Quantitative CrossNational Research Methods
 Pp. 1264955 in International Encyclopedia of the Social and Bellavioral Sciences
, 2001
"... Quantitative nation comparisons pose inevitable tradeoffs. One is that much of the contextual reality of individual nations is sacrificed for the sake of broader generalization. We fail to capture the uniqueness that defines a nation’s culture, historical heritage, and endemic logic. The interpreta ..."
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Quantitative nation comparisons pose inevitable tradeoffs. One is that much of the contextual reality of individual nations is sacrificed for the sake of broader generalization. We fail to capture the uniqueness that defines a nation’s culture, historical heritage, and endemic logic. The interpretation of a variable may, indeed, only be possible when its is studied contextually (Ragin, 1987; and Lieberson, 1991). Boolean analysis, as Ragin argues, helps overcome this dilemma. It has advantages, such as its ability to build conjunctural models with very few cases, and its ability to analyze nonevents. But it needs to be guided by strong theory and substantial knowledge, its applicability is limited to relatively few cases, and it may be too biased in favor of nonadditive, conjunctural models. For an empirical application, see Ragin (1994). See also Section 2.3, no. 72. A second tradeoff has to do with the often limited number of observations available, especially in studies of advanced (OECD) democracies where the N rarely exceeds 25. In broader World comparisons, however, the N approaches 200. Many attempt to supplement few nations with overtime data, as in the case of pooled cross sectional
Limits of and Alternatives to Multiple Regression in MacroComparative Research
, 1999
"... This paper offers criticisms of, and alternatives to, the use of multiple regression in crossnational comparisons of welfare states and comparative political economy generally. To set the context for this discussion, it is worth remembering that there was once quite a clear divide between descripti ..."
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This paper offers criticisms of, and alternatives to, the use of multiple regression in crossnational comparisons of welfare states and comparative political economy generally. To set the context for this discussion, it is worth remembering that there was once quite a clear divide between descriptive and prescriptive studies by historians or social policy analysts—the people who knew what they were talking about; and social scientists—the people who saw welfare states as a convenient source of data for testing abstract theoretical generalizations. This division was inextricably linked to the methodological predilections of each camp. The sociologists and political scientists who worked on welfare states were part of the quantitative revolution in comparative studies. Using correlation and regression analysis, they optimistically 1 This is a work in progress, please shower me with comments and criticisms but don’t cite it yet. I am grateful to the comfortable surroundings and willing ear furnished by Walter Korpi, and a stimulating correspondence with Bruce Western, for rousing me to initially put these ideas on paper. I received extremely helpful comments on earlier drafts from Frank Castles, Peter Hall and Robert Franzese and from participants in the Workshop on Economic Internationalization at Duke University, May 2527 1997 and a seminar at the Wissenschaszentrum Berlin in February of this year.
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
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We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
THE NATURE OF CRIME: CONTINUITY AND CHANGE Changes in the Gender Gap in Crime and Women’s Economic
"... One of the most persistent research findings in criminology is that men commit much more crime than women. This typically is referred to as the gender gap in offending. Many researchers have noted that during the past several decades, women and men have converged in their rates of crime and the gend ..."
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One of the most persistent research findings in criminology is that men commit much more crime than women. This typically is referred to as the gender gap in offending. Many researchers have noted that during the past several decades, women and men have converged in their rates of crime and the gender gap in offending has narrowed. Several explanations of this convergence have been proposed, including the economic marginalization hypothesis, which argues that the gender gap in crime has narrowed because women have experienced increasing economic hardship relative to men. This article reviews research on changes over time in the relative crime of women and men. It presents an analysis of Uniform Crime Reports data on the gender gap in offending from 1960 to 1997 and concludes that there has been an appreciable narrowing of the gap over this period in both property and violent offenses. The article then assesses the evidence in the criminological literature regarding the possible reasons for these changes, including the economic marginalization hypothesis. It then reviews evidence from demographic and economic research regarding the increasing marginalization of women and concludes that changes in the gender gap in crime are consistent with the findings of this research. Finally, the article proposes avenues for extending and refining the economic marginalization perspective. A B