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Quantifying causal effects from observed data using quasi-intervention.

Jinghua Yang1,2, Yaping Wan3,4, Qianxi Ni5,6

  • 1School of Computer Science, University of South China, Hengyang, China. y1062734273@163.com.

BMC Medical Informatics and Decision Making
|December 21, 2022
PubMed
Summary

Quasi-intervention quantifies causal effects in observational data for medical decision-making. This method accurately assesses treatment impacts on disease outcomes, like lung cancer survival, without clinical trials.

Keywords:
Causal effectDo-algorithmInterventionQuasi-experimental designQuasi-intervention

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Area of Science:

  • Medical research
  • Causal inference
  • Biostatistics

Background:

  • Causal inference is vital for medical decision-making, with methods split into observational and experimental studies.
  • Experimental studies face limitations in patient exposure control due to ethical, financial, and resource constraints.
  • Existing observational designs often struggle to establish causality and determine optimal treatment dosages.

Purpose of the Study:

  • To introduce a novel experimental strategy, quasi-intervention, for quantifying causal effects from observational data.
  • To adapt causal inference methods for analyzing treatment options and their impact on disease.
  • To address limitations in current observational research for determining causal relationships and treatment dosages.

Main Methods:

  • Developed a quasi-intervention strategy using causal inference.
  • Converted potential treatment effects into differences in conditional probability.
  • Evaluated the method by analyzing neutrophil-to-lymphocyte ratio (NLR) impact on overall survival (OS) in lung cancer patients.

Main Results:

  • The quasi-intervention method accurately quantified the causal effect of NLR adjustment on lung cancer patient OS.
  • Results were validated against nine existing cohort studies on NLR and lung cancer prognosis.
  • The findings confirm the accuracy and reliability of the quasi-intervention approach.

Conclusions:

  • Quasi-intervention offers a promising approach for quantifying causal effects without requiring clinical trials.
  • The method can enhance confidence in the efficacy and safety of medical treatment options.
  • This strategy provides a valuable tool for analyzing observational medical data.