Harnessing causal forests for epidemiologic research: key considerations.
Am J Epidemiol
; 193(6): 813-818, 2024 06 03.
Article
in En
| MEDLINE
| ID: mdl-38319713
ABSTRACT
Assessing heterogeneous treatment effects (HTEs) is an essential task in epidemiology. The recent integration of machine learning into causal inference has provided a new, flexible tool for evaluating complex HTEs causal forest. In a recent paper, Jawadekar et al (Am J Epidemiol. 2023;192(7)1155-1165) introduced this innovative approach and offered practical guidelines for applied users. Building on their work, this commentary provides additional insights and guidance to promote the understanding and application of causal forest in epidemiologic research. We start with conceptual clarifications, differentiating between honesty and cross-fitting, and exploring the interpretation of estimated conditional average treatment effects. We then delve into practical considerations not addressed by Jawadekar et al, including motivations for estimating HTEs, calibration approaches, and ways to leverage causal forest output with examples from simulated data. We conclude by outlining challenges to consider for future advancements and applications of causal forest in epidemiologic research.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Causality
/
Machine Learning
Type of study:
Guideline
Limits:
Humans
Language:
En
Journal:
Am J Epidemiol
Year:
2024
Document type:
Article
Affiliation country: