Identification of in-sample positivity violations using regression trees: The PoRT algorithm

Abstract

The positivity assumption is crucial when drawing causal inferences from observational studies, but it is often overlooked in practice. A violation of positivity occurs when the sample contains a subgroup of individuals with an extreme relative frequency of experiencing one of the levels of exposure. To correctly estimate the causal effect, we must identify such individuals. For this purpose, we suggest a regression tree-based algorithm.

Keywords

• causal inference
• common support
• decision tree
• experimental treatment assignment
• identifiability
• target population
• 62-04
• 68w40
• 92c60