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A Selection Bias Approach to Sensitivity Analysis for Causal Effects

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  • Blackwell, Matthew

Abstract

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Suggested Citation

  • 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.
  • Handle: RePEc:cup:polals:v:22:y:2014:i:02:p:169-182_01
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    Cited by:

    1. 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.
    2. 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.
    3. Harsh Parikh & Marco Morucci & Vittorio Orlandi & Sudeepa Roy & Cynthia Rudin & Alexander Volfovsky, 2023. "A Double Machine Learning Approach to Combining Experimental and Observational Data," Papers 2307.01449, arXiv.org, revised Apr 2024.
    4. Reeves, Aaron, 2021. "The health effects of wage setting institutions: how collective bargaining improves health but not because it reduces inequality," LSE Research Online Documents on Economics 113422, London School of Economics and Political Science, LSE Library.
    5. Allan Dafoe, 2018. "Nonparametric Identification of Causal Effects under Temporal Dependence," Sociological Methods & Research, , vol. 47(2), pages 136-168, March.

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