Front-Door Versus Back-Door Adjustment with Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments

• Activity Number: 609
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Social Statistics Section
• Author(s): Adam Glynn*+ and Konstantin Kashin
• Companies: Harvard University and Harvard University
• Keywords: causal ; program evaluation ; sensitivity analysis

Abstract

In this paper, we develop bias formulas for front-door estimates and front-door/back-door hybrid estimates of average treatment effects under general patterns of measured and unmeasured confounding. These bias formulas allow for sensitivity analysis, and also allow for comparisons of the bias resulting from standard back-door covariate adjustments (also known as direct adjustment and standardization). We also present these bias comparisons in two special cases: linear structural equation models and nonrandomized program evaluations with one-sided noncompliance. These comparisons demonstrate that there are broad classes of applications for which the front-door or hybrid adjustments will be preferred to the back-door adjustments. We illustrate this point with an application to the National JTPA (Job Training Partnership Act) Study, showing that by using information on enrollment in addition to pre-treatment covariates, the front-door approach provides estimates that are closer to the experimental benchmark than the back-door approach.