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Front-Door Versus Back-Door Adjustment With Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments With Application to a Job Training Program

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  • Adam N. Glynn
  • Konstantin Kashin

Abstract

We demonstrate that the front-door adjustment can be a useful alternative to standard covariate adjustments (i.e., back-door adjustments), even when the assumptions required for the front-door approach do not hold. We do this by providing asymptotic bias formulas for the front-door approach that can be compared directly to bias formulas for the back-door approach. In some cases, this allows the tightening of bounds on treatment effects. We also show that under one-sided noncompliance, the front-door approach does not rely on the use of control units. This finding has implications for the design of studies when treatment cannot be withheld from individuals (perhaps for ethical reasons). We illustrate these points with an application to the National Job Training Partnership Act Study.

Suggested Citation

  • Adam N. Glynn & Konstantin Kashin, 2018. "Front-Door Versus Back-Door Adjustment With Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments With Application to a Job Training Program," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1040-1049, July.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:523:p:1040-1049
    DOI: 10.1080/01621459.2017.1398657
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    Cited by:

    1. Romy Müller & Franziska Kessler & David W. Humphrey & Julian Rahm, 2021. "Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems," Future Internet, MDPI, vol. 13(6), pages 1-36, June.
    2. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    3. Ali Tafti & Galit Shmueli, 2020. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure," Information Systems Research, INFORMS, vol. 31(4), pages 1183-1199, December.
    4. Shantanu Gupta & Zachary C. Lipton & David Childers, 2020. "Estimating Treatment Effects with Observed Confounders and Mediators," Papers 2003.11991, arXiv.org, revised Jun 2021.

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