The promise of multi-site heterogeneity for multiple negative control outcomes

Music City Center 

The use of negative control outcomes (NCOs) has gained much attention in recent years. However, many existing methods rely on the validity of the negative control, which may break down when invalid NCOs exist. We propose a new method to estimate average causal effects that allows the existence of invalid negative outcomes. The key idea is to utilize the heterogeneity among multiple datasets to distinguish the valid and invalid NCOs. First, we give the identification of the causal effect under the multi-site NCO framework. With the usage of multi-site data, we avoid the majority rule that is commonly assumed in related literature. Then, we provide an estimation method based on the identification condition, and we establish the asymptotic properties of the proposed estimator. We conduct simulations to illustrate the good performance of the proposed estimator. We applied our method to GLP1 data to investigate the effectiveness of the drug.

Causal inference

Multi-site

Negative control outcomes 

Section on Statistics in Epidemiology