Quantifying causal effects from observed data using quasi-intervention

• Published in: BMC medical informatics and decision making Vol. 22; no. 1; pp. 337 - 11
• Main Authors: Yang, Jinghua, Wan, Yaping, Ni, Qianxi, Zuo, Jianhong, Wang, Jin, Zhang, Xiapeng, Zhou, Lifang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 21.12.2022; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Biomedical Research; Care and treatment; Causal effect; Causal inference; Causality; Cohort Studies; Diagnosis; Do-algorithm; Epidemiology; Health aspects; Humans; Intervention; Lung cancer; Methods; Probability; Quasi-experimental design; Quasi-intervention; Research Design; Treatment Outcome; China; Intervention; Causal effect; Quasi-intervention; Do-algorithm; Quasi-experimental design

Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental st...