Causal Analysis Task
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A Causal Analysis Task is a analysis task that involves the use of a causal model for causal inference.
- Context:
- It can (typically) involve identifying relationships between cause and effect in various domains, including statistics, epidemiology, and social sciences.
- It can (often) require the establishment of four elements: correlation, sequence in time, a plausible physical or information theory mechanism, and eliminating the possibility of common and alternative causes.
- It can (often) involve artificial or natural experiments.
- ...
- Example(s):
- Counter-Example(s):
- 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).
References
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."