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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]
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 ...
Judea Pearl was born in Tel Aviv, British Mandate for Palestine, in 1936 to Eliezer and Tova Pearl, who were Polish Jewish immigrants, grew up in Bnei Brak. His grandfather Chaim Pearl was one of Bnei Brak's founders. [8] [9] [10] He is a descendant of Menachem Mendel of Kotzk on his mother's side.
The Halpern-Pearl definitions of causality take account of examples like these. [28] The first and third Halpern-Pearl conditions are easiest to understand: AC1 requires that Alice threw the brick and the window broke in the actual work. AC3 requires that Alice throwing the brick is a minimal cause (cf. blowing a kiss and throwing a brick).
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.
This resolution of Lord’s Paradox answers both questions: (1) How to allow for preexisting differences between groups and (2) Why the data appear paradoxical. Pearl's do-calculus [6] further answers question (1) for any causal model assumed, including models with multiple unobserved confounders.
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process.