Causal Relationship
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A Causal Relationship is a asymmetric physical relationship between an event (a causing event) and a mandatory effect (a caused effect) in the world.
- AKA: Causal Interaction, Cause-and-Effect Relationship.
- Context:
- It can be a Causal Relation, Causal Function, Causal Operation.
- It can be referenced by a Causal Inference, such as a prediction.
- It can be represented in a Causal Network or a Causal Model.
- It can be determined by Causal Analysis and Experimental Design.
- It can be challenged by issues such as Confounding Factors, Spurious Correlation, and Reverse Causation.
- …
- Example(s):
- [math]\displaystyle{ s \lt c }[/math]
- [math]\displaystyle{ e=mc^2 }[/math], illustrating the relationship between mass (m) and energy (e) with the speed of light (c) as a constant.
- Hemoglobin Protein Folding.
- The relationship between smoking and lung cancer, where smoking is a causing event and lung cancer is a caused effect.
- …
- Counter-Example(s):
- A Correlational Relationship.
- A Semantic Relationship, like synonymy.
- A Mutually Dependent Relationship, such as a correlational relationship
- A Feedback Loop, where the effect also influences the cause.
- See: Aristotle's Four Causes, Causal Loop, Efficient Cause, Final Cause, Interventional Causality, Material Cause, Probabilistic Causation, Temporal Precedence, Causal Semantic Relation, Counterfactual Relationship, Randomized Controlled Trial, Granger Causality, Causal Inference, Impact Evaluastion.
References
2024
- (Wikipedia, 2024) ⇒ https://en.wikipedia.org/wiki/Causality Retrieved:2024-1-19.
- Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such a basic concept, it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. [2] Accordingly, causality is implicit in the logic and structure of ordinary language, as well as explicit in the language of scientific causal notation.
In English studies of Aristotelian philosophy, the word "cause" is used as a specialized technical term, the translation of Aristotle's term αἰτία, by which Aristotle meant "explanation" or "answer to a 'why' question". Aristotle categorized the four types of answers as material, formal, efficient, and final "causes". In this case, the "cause" is the explanans for the explanandum, and failure to recognize that different kinds of "cause" are being considered can lead to futile debate. Of Aristotle's four explanatory modes, the one nearest to the concerns of the present article is the "efficient" one.
David Hume, as part of his opposition to rationalism, argued that pure reason alone cannot prove the reality of efficient causality; instead, he appealed to custom and mental habit, observing that all human knowledge derives solely from experience.
The topic of causality remains a staple in contemporary philosophy.
- Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, [1] which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such a basic concept, it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. [2] Accordingly, causality is implicit in the logic and structure of ordinary language, as well as explicit in the language of scientific causal notation.
- ↑ Compare:
- ↑ Whitehead, A.N. (1929). Process and Reality. An Essay in Cosmology. Gifford Lectures Delivered in the University of Edinburgh During the Session 1927–1928, Macmillan, New York; Cambridge University Press, Cambridge UK, "The sole appeal is to intuition."
2018
- (Pearl & Mackenzie, 2018) ⇒ Judea Pearl, and Dana Mackenzie. (2018). “The Book of Why: The New Science of Cause and Effect.” Hachette UK. ISBN:9780465097609
- QUOTE: … it is important to understand the achievements that causal inference has tallied thus far. We will explore the way that it has transformed the thinking of scientists in almost every data-informed discipline and how it is about to change our lives.
- The new science addresses seemingly straightforward questions like these:
- How effective is a given treatment in preventing a disease?
- Did the new tax law cause our sales to go up, or was it our advertising campaign?
- What is the health-care cost attributable to obesity?
- Can hiring records prove an employer is guilty of a policy of sex discrimination?
- I’m about to quit my job. Should I?
- These questions have in common a concern with cause-and-effect relationships, recognizable through words such as “preventing,” “cause,” “attributable to,” “policy,” and “should I.” Such words are common in everyday language, and our society constantly demands answers to such questions. Yet, until very recently, science gave us no means even to articulate, let alone answer, them. …
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/causality Retrieved:2014-7-27.
- ↑ 'The action of causing; the relation of cause and effect' OED
2012
- (Pearl, 2012) ⇒ Judea Pearl. (2012). “Q&A: A Sure Thing". Interview in Communications of the ACM, 55(6). doi:10.1145/2184319.2184347
- QUOTE: There are three levels of causal relationships. The zero level, which is the level of associations, not causation, deals with the question “What is?” The second level is “What if?” And the third level is “Why?” That’s the counterfactual level. Initially, I thought of counterfactuals as something for philosophers to deal with. Now I see them as just the opposite. They are the building blocks of scientific understanding.
2011
- (Silva, 2011) ⇒ Ricardo Silva. (2011). “Causality.” In: (Sammut & Webb, 2011) p.159
2009
- (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=causal
- # S: (adj) causal (involving or constituting a cause; causing) "a causal relationship between scarcity and higher prices"
- http://en.wikipedia.org/wiki/Causality
- Causality is the relationship between an event (the cause) and a second event (the effect), where the second event is a consequence of the first.
2007
- (Torbeck, 2007) ⇒ Lynn D. Torbeck. (2007). “Pharmaceutical and Medical Device Validation by Experimental Design." CRC Press. ISBN:1420055690
- QUOTE: Controlled multivariate experiments are the most logical, the most scientific, and the most efficient way that scientists know to collect data. … These observational tools cannot find and describe cause-and-effect relationships directly. The only way to find these relationships is to conduct a multivariate controlled-experiment.
In contrast to the observational approach, data collection in an controlled experiment is active; investigators take control of the environment and critical process parameters. By deliberate changes in key factors, the cause-and-effect relationships are forced to show themselves.
- QUOTE: Controlled multivariate experiments are the most logical, the most scientific, and the most efficient way that scientists know to collect data. … These observational tools cannot find and describe cause-and-effect relationships directly. The only way to find these relationships is to conduct a multivariate controlled-experiment.
2006
- (Choi & Scholl, 2006) ⇒ Hoon Choi, and Brian J. Scholl. (2006). “Perceiving Causality After the Fact: Postdiction in the Temporal Dynamics of Causal Perception.” In: PERCEPTION-LONDON- 35, no. 3.
2000
- (Pearl, 2000) ⇒ Judea Pearl. (2000). “Causality: Models, reasoning, and inference." Cambridge University Press, ISBN:0521773628
- QUOTE: … this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.