Causal Analysis Task

A Causal Analysis Task is an analysis task that involves the use of a causal model for causal inference (supporting cause-effect relationship identification through systematic causal investigation).

• AKA: Causality Analysis Task, Cause-Effect Analysis, Causal Relationship Study.
• Context:
  • Task Input: System States, Event Sequences, Variable Relationships
    • Optional Input: Historical Data, Experimental Design, Domain Knowledge
  • Task Output: Causal Model, Causal Relationships, Causation Evidence
  • Task Performance Measure: Analysis Quality Metrics such as causal strength, relationship confidence, and alternative elimination
  • ...
  • It can establish Causal Relationships through systematic investigation.
  • It can identify Correlation Patterns through statistical analysis.
  • It can verify Temporal Sequences through time-based analysis.
  • It can determine Causal Mechanisms through physical process analysis.
  • It can eliminate Alternative Causes through systematic exclusion.
  • ...
  • It can often require Experimental Design during causation testing.
  • It can often utilize Natural Experiments during observational analysis.
  • It can often perform Statistical Testing during relationship validation.
  • It can often apply Information Theory during mechanism modeling.
  • It can often maintain Causal Documentation during analysis recording.
  • ...
  • It can range from being a Simple Causal Analysis to being a Complex Causal Analysis, depending on its relationship complexity.
  • It can range from being an Observational Causal Analysis to being an Experimental Causal Analysis, depending on its investigation method.
  • It can range from being a Domain-Specific Analysis to being a Cross-Domain Analysis, depending on its analysis scope.
  • ...
  • It can integrate with Statistical Analysis for correlation testing.
  • It can connect to Experimental Design for causation validation.
  • It can support Predictive Modeling for outcome forecasting.
  • It can link to Domain Analysis for context understanding.
  • ...
• Examples:
  • Scientific Causal Analysises, such as:
    • Medical Analysises, such as:
      • Medical Pathology Analysis for disease causation study.
      • Epidemiological Study for disease spread analysis.
    • Research Analysises, such as:
      • Social Science Analysis for behavioral cause study.
      • Economic Analysis for market impact study.
  • Technical Causal Analysises, such as:
    • System Analysises, such as:
      • Fault Analysis for failure cause identification.
      • Performance Analysis for bottleneck cause study.
    • Process Analysises, such as:
      • Quality Issue Analysis for defect cause study.
      • Efficiency Analysis for optimization barrier study.
  • ...
• Counter-Examples:
  • Descriptive Analysis Task, which focuses on pattern description rather than causation identification.
  • Predictive Analysis Task, which emphasizes outcome forecasting over cause determination.
  • Correlational Analysis, which identifies relationships without establishing causation.
• See: Exposition (Narrative), Convention (Norm), Causal Relation, Natural Experiment, Design of Experiments, Statistics, Correlation, Information Theory, Common Cause And Special Cause (Statistics), Exploratory Causal Analysis (ECA).

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References

• https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&as_ylo=2020&as_vis=1&q=data-driven+%22causal+analysis%22&btnG =

2024

• (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Causal_analysis Retrieved:2024-1-19.
  • Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.

2000

• (Pearl, 2000) ⇒ Judea Pearl. (2000). “Causality: Models, Reasoning and Inference.” Cambridge University Press.
  • QUOTE: It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics.

2011

• (Retherford & Choe, 2011) ⇒ RD Retherford, MK Choe. (2011). “Statistical Models for Causal Analysis.” Google Books.
  • QUOTE: "The book discusses whether and how one variable can be said to cause changes in another, addressing the concept of two-way causation. It is particularly useful in the context of bivariate analysis and the pursuit of causal analysis in statistical research."

1996

• (Peyrot, 1996) ⇒ M Peyrot. (1996). “Causal analysis: Theory and application." In: Journal of Pediatric Psychology.
  • QUOTE: "This paper aims to make the concept and implementation of causal analysis more accessible. It delves into the historical foundations of causal analysis, examining the logic of multivariate analysis and its application in establishing causal relationships."

1995

• (Finkel, 1995) ⇒ SE Finkel. (1995). “Causal Analysis with Panel Data.” Google Books.
  • QUOTE: "Focusing on causal inference in nonexperimental research, this book illustrates how panel data can strengthen causal analysis. It is particularly valuable for its insights into multiple regression analysis and causal modeling techniques using panel data."

1975

• (Heise, 1975) ⇒ DR Heise. (1975). “Causal Analysis.” PsycNET.
  • QUOTE: "Introduces advanced techniques of causal analysis, covering topics such as causal diagrams, the principles of flowgraph analysis and loops, and statistical concepts. The book also explores rules for analyzing causal relationships, offering a comprehensive guide for understanding complex causal processes."

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