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Bias mitigation in empirical peace and conflict studies: A short primer on posttreatment variables

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  • Christoph Dworschak

    (Department of Politics, University of York)

Abstract

Posttreatment variables are covariates that are preceded by the main explanatory variable. Their inclusion in a statistical model does not ‘control’ for their influence on the relationship of interest, and it does not substitute for a mediation analysis. Likewise, a coefficient estimate of an appropriate ‘control variable’ cannot be interpreted as a causal effect estimate. While these facts are well-established in various fields across the social sciences, their recognition in the field of peace and conflict studies is more limited. Originally collected data on recent publications from leading peace and conflict journals reveal that a large majority of evaluated articles condition on posttreatment variables, demonstrating how a review of these fallacies can help to substantially improve future research on peace and conflict. Drawing on a broad set of literature and using graphical approaches, I offer an intuitive explanation of the logic of posttreatment variables and clarify common misconceptions. Building on recent developments in methodology and software, and by deriving conditions for bounding using analytical bias expressions, I discuss avenues for dealing with posttreatment variables in observational studies. The article concludes with a discussion of implications for applied research.

Suggested Citation

  • Christoph Dworschak, 2024. "Bias mitigation in empirical peace and conflict studies: A short primer on posttreatment variables," Journal of Peace Research, Peace Research Institute Oslo, vol. 61(3), pages 462-476, May.
    • Handle: RePEc:sae:joupea:v:61:y:2024:i:3:p:462-476
    DOI: 10.1177/00223433221145531
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    References listed on IDEAS

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    1. Adam N. Glynn, 2012. "The Product and Difference Fallacies for Indirect Effects," American Journal of Political Science, John Wiley & Sons, vol. 56(1), pages 257-269, January.
    2. Imai, Kosuke & Keele, Luke & Tingley, Dustin & Yamamoto, Teppei, 2011. "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies," American Political Science Review, Cambridge University Press, vol. 105(4), pages 765-789, November.
    3. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    4. Hannah Smidt, 2020. "Mitigating election violence locally: UN peacekeepers’ election-education campaigns in Côte d’Ivoire," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(1), pages 199-216, January.
    5. Blackwell, Matthew & Glynn, Adam N., 2018. "How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables," American Political Science Review, Cambridge University Press, vol. 112(4), pages 1067-1082, November.
    6. Acharya, Avidit & Blackwell, Matthew & Sen, Maya, 2016. "Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects," American Political Science Review, Cambridge University Press, vol. 110(3), pages 512-529, August.
    7. Keele, Luke & Stevenson, Randolph T. & Elwert, Felix, 2020. "The causal interpretation of estimated associations in regression models," Political Science Research and Methods, Cambridge University Press, vol. 8(1), pages 1-13, January.
    8. Carlos Cinelli & Chad Hazlett, 2020. "Making sense of sensitivity: extending omitted variable bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(1), pages 39-67, February.
    9. Blackwell, Matthew, 2014. "A Selection Bias Approach to Sensitivity Analysis for Causal Effects," Political Analysis, Cambridge University Press, vol. 22(2), pages 169-182, April.
    10. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    11. Shpitser Ilya & VanderWeele Tyler J, 2011. "A Complete Graphical Criterion for the Adjustment Formula in Mediation Analysis," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-24, March.
    12. Jacob M. Montgomery & Brendan Nyhan & Michelle Torres, 2018. "How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It," American Journal of Political Science, John Wiley & Sons, vol. 62(3), pages 760-775, July.
    13. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
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