Learning Causal Effects From Observational Data in Healthcare: A Review and Summary.

ABSTRACT: Causal inference is a broad field that seeks to build and apply models that learn the effect of interventions on outcomes using many data types. While the field has existed for decades, its potential to impact healthcare outcomes has increased dramatically recently due to both advancements in machine learning and the unprecedented amounts of observational data resulting from electronic capture of patient claims data by medical insurance companies and widespread adoption of electronic health records (EHR) worldwide. However, there are many different schools of learning causality coming from different fields of statistics, some of them strongly conflicting. While the recent advances in machine learning greatly enhanced causal inference from a modeling perspective, it further exacerbated the

SUBMITTER: Shi J 

PROVIDER: S-EPMC9300826 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature