MSR36 Current Applications of Machine Learning for Causal Inference in Healthcare Research Using Observational Data
Year: 2024
Type: conference-abstract
Abstract: Machine learning (ML) approaches have the potential to facilitate causal inference using large, multidimensional real-world data (RWD). However, the implementation of ML-based approaches in answering causal questions can be conceptually and computationally complex. To address this challenge, we used findings from a literature review to visually illustrate the relationships between different types of barriers to causal inference in healthcare research. We further examined the landscape of ML applications which have been targeted at addressing these barriers.
Source: Value in Health
Authors: O Onasanya, S Hoffman, K Harris, R Dixon, M Grabner
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,200
OpenAlex ID: W4399707212