Causal Relationship

A causal relationship is a relationship between an event (a Causing Event) and a mandatory effect (a Caused Effect) in the world.

• AKA: Causal Interaction.
• 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.
  • …
• Example(s):
  • [math]s \lt c[/math]
  • [math]e=mc^2[/math]
  • Hemoglobin Protein Folding.
  • …
• Counter-Example(s):
  • a Semantic Relationship.
  • a Mutually Dependent Relationship, such as a correlational relationship.
• See: Causal Semantic Relation, Counterfactual Relationship; Graphical Models; Learning Graphical Models.

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References

2014

• (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/causality Retrieved:2014-7-27.
  • Causality (also referred to as causation ) is the relation between an event (the cause) and a second event (the effect), where the second event is understood as a physical consequence of the first.

    In common usage, causality is also the relation between a set of factors (causes) and a phenomenon (the effect). Anything that affects an effect is a factor of that effect. A direct factor is a factor that affects an effect directly, that is, without any intervening factors. (Intervening factors are sometimes called "intermediate factors".) The connection between a cause(s) and an effect in this way can also be referred to as a causal nexus.

    Causes and effects are typically related to changes, events, or processes; such causes are Aristotle's moving causes. The word 'cause' is also used to mean 'explanation' or 'answer to a why question', including Aristotle's material, final, and formal causes; then the 'cause' is the explanans while the 'effect' is the explanandum. In this case, there are various recognizable kinds of 'cause'; candidates include objects, processes, properties, variables, facts, and states of affairs; failure to recognize that different kinds of 'cause' are being considered can lead to debate.

    The philosophical treatment on the subject of causality extends over millennia. In the Western philosophical tradition, discussion stretches back at least to Aristotle, and the topic remains a staple in contemporary philosophy.

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.

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.

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