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Bias Amplification and Bias Unmasking

Author

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  • Middleton, Joel A.
  • Scott, Marc A.
  • Diakow, Ronli
  • Hill, Jennifer L.

Abstract

In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates may increase bias, and the mechanisms underlying this phenomenon have not been fully explored. We add to the literature on bias-increasing covariates by first introducing a way to decompose omitted variable bias into three constituent parts: bias due to an unobserved confounder, bias due to excluding observed covariates, and bias due to amplification. This leads to two important findings. Although instruments have been the primary focus of the bias amplification literature to date, we identify the fact that the popular approach of adding group fixed effects can lead to bias amplification as well. This is an important finding because many practitioners think that fixed effects are a convenient way to account for any and all group-level confounding and are at worst harmless. The second finding introduces the concept of bias unmasking and shows how it can be even more insidious than bias amplification in some cases. After introducing these new results analytically, we use constructed observational placebo studies to illustrate bias amplification and bias unmasking with real data. Finally, we propose a way to add bias decomposition information to graphical displays for sensitivity analysis to help practitioners think through the potential for bias amplification and bias unmasking in actual applications.

Suggested Citation

  • Middleton, Joel A. & Scott, Marc A. & Diakow, Ronli & Hill, Jennifer L., 2016. "Bias Amplification and Bias Unmasking," Political Analysis, Cambridge University Press, vol. 24(3), pages 307-323, July.
  • Handle: RePEc:cup:polals:v:24:y:2016:i:03:p:307-323_01
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    Citations

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    Cited by:

    1. Steiner Peter M. & Kim Yongnam, 2016. "The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-22, September.
    2. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    3. Yongnam Kim, 2019. "The Causal Structure of Suppressor Variables," Journal of Educational and Behavioral Statistics, , vol. 44(4), pages 367-389, August.
    4. Bo Zhang & Dylan S. Small, 2020. "A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1285-1305, November.

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