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[Causality in objective world: Directed Acyclic Graphs-based structural parsing].

Y J Zheng1, N Q Zhao2, Y N He1

  • 1Department of Public Health Microbiology of School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Key Laboratory of Health Technology Assessment, Ministry of Health, Fudan University, Shanghai 200032, China.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
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Summary
This summary is machine-generated.

This study introduces a novel framework for causality research by dividing the objective world into time zones and using Directed Acyclic Graphs (DAGs) to model causal relationships and control confounding.

Keywords:
Causal WebCausalityConfoundingDirected Acyclic GraphsTemporality

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Area of Science:

  • Causality research
  • Complex systems modeling
  • Statistical inference

Background:

  • Causality research faces challenges due to the complex nature of causal relationships in the objective world.
  • Existing methods often struggle to fully capture the temporal dynamics and confounding factors inherent in causal inference.

Purpose of the Study:

  • To propose a structured framework for understanding and researching causality in the objective world.
  • To develop a method for parsing causal relationships using temporal information and Directed Acyclic Graphs (DAGs).
  • To clarify the structural basis for controlling confounding in effect estimation.

Main Methods:

  • Dividing the objective world into three time zones and two time points based on temporality.
  • Utilizing Directed Acyclic Graphs (DAGs) to represent causal relationships within and across time zones.
  • Deconstructing the causal web into a core component and internal DAGs within each time unit.

Main Results:

  • The proposed framework models causal relationships by considering temporal sequences and variable interactions.
  • The causal web is structured into a core DAG and time-unit-specific internal DAGs.
  • The approach provides a clear structural basis for identifying and controlling confounding variables.

Conclusions:

  • The developed causality frames using DAGs offer a systematic approach to objective world research.
  • This temporal and structural decomposition aids in a clearer understanding of cause-effect relationships.
  • The framework facilitates improved control of confounding for more accurate effect estimation.