Discover the Factors of Longevity by Causal Discovery with PC Algorithm and Neural Networks
DOI:
https://doi.org/10.61173/311n9348Keywords:
PC algorithm, Neural network, causal discovery, counterfactual inference, structural equation modelingAbstract
The study title has been to study the multilayered factors that govern a human prospect of living through the application of the PC Algorithm (Graph-Based Algorithm), logistic regression analysis, and the neural networks in combination. To realign the research purpose, a vast array of data can be used, which included socioeconomic status information alongside health and environmental aspects carefully chosen for their likely influence on how long people can live. This article will explain the longevity determinants by examining not only the conditions but also the environmental and health indicators and their combinatory effects. The PC algorithm can produce a directed acyclic graph (DAG) to see how elements like healthcare spending, education, and CO2 emissions are correlated with human longevity and how they influence each other. Moreover, the research takes other examples by analysis: researchers can examine hypothetical events. Neural networks were employed to depict the interrelationship among these variables which subsequently provided a user-appropriate understanding of their sudden joint influence. Consequently, a hypothesize was purposed that the potential interventions for the elongation of human life. Policy-making researchers can gain a better representation of the situation of health inequality and the new recommended solutions to tackle this problem and increase life expectancy by implementing complex analysis tools to blows away the criminal aspects of human longevity.
References
[1] Spirtes P, Glymour C, Scheines R. Causation, Prediction, and Search (2nd ed.). MIT Press, 2000.
[2] Zhang K, Hyvärinen A. A general framework for discovering causal relations in time series data. Journal of Machine Learning Research, 2018, 19: 1-30.
[3] Pearl J. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.
[4] Siontis G C M, et al. Logistic regression was as good as machine learning for predicting major chronic diseases. PubMed, 2020.
[5] Bächtiger A, et al. Causal Inference in Statistics: A Primer. MDPI, 2021.
[6] Kosinski M. Why use logistic regression instead of a neural network? Cross Validated. Epidemiology, 2019.
[7] Greenland S, et al. Causal diagrams for epidemiologic research. Epidemiology, 1999, 10(1): 37-48.
[8] Peters J, Janzing D, Schölkopf B. Elements of Causal Inference: Foundations and Learning Algorithms. MIT Press, 2017.
[9] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer, 2019.
[10] Rubin D B. Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 2005, 100(469): 322-331.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
