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Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for causal AI is for organisations to explain decision-making and the causes for a decision.
Causality: Models, Reasoning, and Inference (2000; [1] updated 2009 [2]) is a book by Judea Pearl. [3] It is an exposition and analysis of causality. [4] [5] It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in several fields including statistics, computer science and epidemiology. [6]
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.
The Book of Why: The New Science of Cause and Effect is a 2018 nonfiction book by computer scientist Judea Pearl and writer Dana Mackenzie. The book explores the subject of causality and causal inference from statistical and philosophical points of view for a general audience.
Judea Pearl defines a causal model as an ordered triple ,, , where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U ...
Greenland, Robins and Pearl [11] note an early use of the term "confounding" in causal inference by John Stuart Mill in 1843. Fisher introduced the word "confounding" in his 1935 book "The Design of Experiments" [ 12 ] to refer specifically to a consequence of blocking (i.e., partitioning ) the set of treatment combinations in a factorial ...
Causal research, is the investigation of (research into) cause-relationships. [ 1 ] [ 2 ] [ 3 ] To determine causality, variation in the variable presumed to influence the difference in another variable(s) must be detected, and then the variations from the other variable(s) must be calculated (s).
Mill's methods are five methods of induction described by philosopher John Stuart Mill in his 1843 book A System of Logic. [ 1 ] [ 2 ] They are intended to establish a causal relationship between two or more groups of data, analyzing their respective differences and similarities.