Bounds on Controlled Direct Effects Under Monotonic Assumptions About Mediators and Confounders

• Activity Number: 360
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yasutaka Chiba*+
• Companies: Kinki University School of Medicine
• Keywords: causal inference ; potential outcome ; randomized trial

Abstract

Adjusting for intermediate variables is a common analytic strategy for estimating a direct effect. Even if the total effect is unconfounded, the direct effect is not identified when unmeasured variables affect the intermediate and outcome variables. Therefore, Cai et al. (Biometrics 2008; 64:695-701) presented bounds on controlled direct effects via linear programming. They applied a monotonic assumption about treatment and intermediate variables and a no-interaction assumption to derive narrower bounds. We improve their bounds without using linear programming and hence derive a bound under a monotonic assumption about an intermediate variable only. To improve the bounds, we further introduce a monotonic assumption about confounders presented by VanderWeele (Biometrics 2008; 64:702-706). While Cai et al. (2008) assumed that an outcome is a binary variable, we do not make that assumption.