5 papers
A Weighting Framework for Clusters as Confounders in Observational Studies
Eli Ben-Michael, Avi Feller, Luke Keele
When units in observational studies are clustered in groups, such as students in schools or patients in hospitals, researchers often address confounding by adjusting for cluster-le…
Forest Kernel Balancing Weights: Outcome-Guided Features for Causal Inference
Andy A. Shen, Eli Ben-Michael, Avi Feller +2
While balancing covariates between groups is central for observational causal inference, selecting which features to balance remains a challenging problem. Kernel balancing is a pr…
Sensitivity Analysis for Clustered Observational Studies with an Application to the Effectiveness of Magnet Nursing Hospitals
Melody Huang, Eli Ben-Michael, Matthew McHugh +1
In a clustered observational study, treatment is assigned to groups and all units within the group are exposed to the treatment. Here, we use a clustered observational study (COS)…
Approximate Balancing Weights for Clustered Observational Study Designs
Luke Keele, Eli Ben-Michael, Lindsay Page
In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustmen…
Using Balancing Weights to Target the Treatment Effect on the Treated when Overlap is Poor
Eli Ben-Michael, Luke Keele
Inverse probability weights are commonly used in epidemiology to estimate causal effects in observational studies. Researchers can typically focus on either the average treatment e…