counterfactuals and causal inference pdf

Counterfactuals and causal inference pdf

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On the Role of Counterfactuals in Inferring Causal Effects

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This volume is a major contribution to our understanding of causality in observational social science. Morgan and Winship use a sophisticated counterfactual understanding of causality as a framework to integrate three major sets of methods for casual inference: statistical methods involving regression and matching analysis, instrumental variable techniques, and the specification of causal mechanisms. The analysis proceeds in a comprehensive and logical fashion, illustrating its arguments with intuitively appealing graphs and with a running set of empirical examples, such as the debates stimulated by the work of James Coleman and his associates on the impact of Catholic schooling on student achievement. Counterfactuals and Causal Inference is an important work that is likely to become required reading in courses on research design and causal inference in sociology and political science. It is written so clearly, with its major points explained in straightforward prose, that it will be of great value to students and faculty whose work is principally qualitative, as well as to the quantitatively oriented audience to which it is principally directed. The authors build on a venerable tradition in social science, expressed by Samuel A. Holland called the Fundamental Problem of Causal Inference: one cannot simultaneously observe the treatment and control conditions for the same unit.

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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Morgan , Christopher Winship Published Psychology. Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2.

Metrics details. The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation.

On the Role of Counterfactuals in Inferring Causal Effects

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4 comments

  • Serge D. 04.05.2021 at 08:53

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  • AgnГЁs C. 05.05.2021 at 05:44

    Causal inference in the empiricalsciences is based on counterfactuals.

    Reply
  • Rex H. 12.05.2021 at 02:02

    In this book, the counterfactual model of causality for observa- tional data analysis is presented, and methods for causal effect estimation are demonstrated using.

    Reply

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