227,632 research outputs found

    Subjective Causality and Counterfactuals in the Social Sciences

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    The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization

    EI: A Program for Ecological Inference

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    The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King'97). Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological") data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act) and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.

    What Foundations for Statistical Modeling and Inference?

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    The primary aim of this article is to review the above books in a comparative way from the standpoint of my perspective on empirical modeling and inference. 1 Hacking (1965). Logic of Statistical Inference 2. Mayo (2018). Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars 3. Conclusion

    Contrasting Revealed Comparative Advantages when Trade is (also)in Intermediate Products

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    The paper reviews and compare a selection of existing and new alternative indicators of Revealed Comparative Advantages, with a special emphasis on trade in intermediate products. The research adopts a statistical approach for both its theoretical and its analytical facets. The formal concepts are those used —inter alia—in statistical inference and information theory. The empirical part applies Exploratory Data Analysis on trade and production data from OECD’s Inter-Country Input-Output Tables. International Input-Output data introduce a new dimension in the definition of comparative advantages: upstream or downstream competitiveness. It is shown that One-Way and Two-Way trade indices capture different aspects of trade competitiveness, and are complementary. Comparative advantages being relative by definition, ordinal or dichotomous classifications provide more robust results than the absolute cardinal indices. Even with dichotomous indicators, the classification of best performers remains blurry, fuzziness varying greatly among product categories

    Analyzing Granger causality in climate data with time series classification methods

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    Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
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