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Graphical Causal Models

  • Felix ElwertEmail author
Chapter
Part of the Handbooks of Sociology and Social Research book series (HSSR)

Abstract

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto the associations and independencies in observable data. The two primary uses of DAGs are (1) determining the identifiability of causal effects from observed data and (2) deriving the testable implications of a causal model. Concepts covered in this chapter include identification, d-separation, confounding, endogenous selection, and overcontrol. Illustrative applications then demonstrate that conditioning on variables at any stage in a causal process can induce as well as remove bias, that confounding is a fundamentally causal rather than an associational concept, that conventional approaches to causal mediation analysis are often biased, and that causal inference in social networks inherently faces endogenous selection bias. The chapter discusses several graphical criteria for the identification of causal effects of single, time-point treatments (including the famous backdoor criterion), as well identification criteria for multiple, time-varying treatments.

Keywords

Causal Effect Causal Model Unit Treatment Causal Path Offer Wage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

I thank Stephen Morgan, Judea Pearl, Tyler VanderWeele, Xiaolu Wang, Christopher Winship, and my students in Soc 952 at the University of Wisconsin for discussions and advice. Janet Clear and Laurie Silverberg provided editorial assistance. All errors are mine.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Sociology, Center for Demography and EcologyUniversity of Wisconsin–MadisonMadisonUSA

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