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Using genetic information to test causal relationships in cross-sectional data

Author

Listed:
  • Brad Verhulst

    (Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, USA)

  • Ryne Estabrook

    (Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, USA)

Abstract

Cross-sectional data from twins contain information that can be used to derive a test of causality between traits. This test of directionality is based upon the fact that genetic relationships between family members conform to an established structural pattern. In this paper we examine several common methods for empirically testing causality as well as several genetic models that we build on for the Direction of Causation (DoC) model. We then discuss the mathematical components of the DoC model and highlight limitations of the model and potential solutions to these limitations. We conclude by presenting an example from the personality and politics literature that has begun to explore the question whether or not personality traits cause people to hold specific political attitudes.

Suggested Citation

  • Brad Verhulst & Ryne Estabrook, 2012. "Using genetic information to test causal relationships in cross-sectional data," Journal of Theoretical Politics, , vol. 24(3), pages 328-344, July.
    • Handle: RePEc:sae:jothpo:v:24:y:2012:i:3:p:328-344
    DOI: 10.1177/0951629812439348
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    References listed on IDEAS

    as
    1. Mondak, Jeffery J. & Hibbing, Matthew V. & Canache, Damarys & Seligson, Mitchell A. & Anderson, Mary R., 2010. "Personality and Civic Engagement: An Integrative Framework for the Study of Trait Effects on Political Behavior," American Political Science Review, Cambridge University Press, vol. 104(1), pages 85-110, February.
    2. Brad Verhulst & Lindon J. Eaves & Peter K. Hatemi, 2012. "Correlation not Causation: The Relationship between Personality Traits and Political Ideologies," American Journal of Political Science, John Wiley & Sons, vol. 56(1), pages 34-51, January.
    3. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    4. Medland, Sarah E. & Hatemi, Peter K., 2009. "Political Science, Biometric Theory, and Twin Studies: A Methodological Introduction," Political Analysis, Cambridge University Press, vol. 17(2), pages 191-214, April.
    5. Steven Boker & Michael Neale & Hermine Maes & Michael Wilde & Michael Spiegel & Timothy Brick & Jeffrey Spies & Ryne Estabrook & Sarah Kenny & Timothy Bates & Paras Mehta & John Fox, 2011. "OpenMx: An Open Source Extended Structural Equation Modeling Framework," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 306-317, April.
    6. Gerber, Alan S. & Huber, Gregory A. & Doherty, David & Dowling, Conor M. & Ha, Shang E., 2010. "Personality and Political Attitudes: Relationships across Issue Domains and Political Contexts," American Political Science Review, Cambridge University Press, vol. 104(1), pages 111-133, February.
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