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  2. Causal inference - Wikipedia

    en.wikipedia.org/wiki/Causal_inference

    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.

  3. The Book of Why - Wikipedia

    en.wikipedia.org/wiki/The_Book_of_Why

    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.

  4. Causality (book) - Wikipedia

    en.wikipedia.org/wiki/Causality_(book)

    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]

  5. Markov blanket - Wikipedia

    en.wikipedia.org/wiki/Markov_blanket

    In a Bayesian network, the Markov boundary of node A includes its parents, children and the other parents of all of its children.. In statistics and machine learning, when one wants to infer a random variable with a set of variables, usually a subset is enough, and other variables are useless.

  6. Lord's paradox - Wikipedia

    en.wikipedia.org/wiki/Lord's_paradox

    Unlike descriptive statements (e.g. "the average height in the US is X"), causal statements involve a comparison between what happened and what would have happened absent an intervention. The latter is unobservable in the real world, a fact that Holland & Rubin term "the fundamental problem of causal inference" (pg. 10).

  7. Mill's methods - Wikipedia

    en.wikipedia.org/wiki/Mill's_Methods

    If an instance in which the phenomenon under investigation occurs, and an instance in which it does not occur, have every circumstance save one in common, that one occurring only in the former; the circumstance in which alone the two instances differ, is the effect, or cause, or an indispensable part of the cause, of the phenomenon.

  8. Covariation model - Wikipedia

    en.wikipedia.org/wiki/Covariation_model

    Harold Kelley's covariation model (1967, 1971, 1972, 1973) [1] is an attribution theory in which people make causal inferences to explain why other people and ourselves behave in a certain way. It is concerned with both social perception and self-perception (Kelley, 1973).

  9. Regression discontinuity design - Wikipedia

    en.wikipedia.org/wiki/Regression_discontinuity...

    In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.