Distributional Negative Control Outcome Framework for Calibration of Unmeasured Confounders

Music City Center 

Unobserved confounders have long posed a major challenge in causal inference. Traditional methods that adjust for these confounders use auxiliary variables. In this paper, we propose a new framework that does not model the unobserved confounders directly but rather assumes that their average effects on multiple negative control outcomes follow some unknown prior distribution. Based on this assumption, we achieve identification of the target causal effect distribution. Further, we propose two methods for constructing confidence intervals of the target parameter. We applied our new method to a study on real-world effectiveness of GLP1-RA on mental health conditions using electronic health record data from Penn Medicine Health System.

Bias correction

Causal inference

Distributional identification


Negative control outcomes 

Section on Statistics in Epidemiology