About this document
Hierarchical Models in Causal Inference by Eduardo Índigo is a document available to read on EtoBox.
Hierarchical models are essential for modeling causal effects by accounting for data collection, adjusting for unmeasured covariates, and capturing treatment effect variation. They provide significant advantages over classical approaches, particularly in complex data structures and observational studies. The document discusses recent developments in hierarchical modeling for causal inference and suggests new research directions in this area.
- Author
- Eduardo Índigo
- Language
- EN