Judea Pearl Quotes (9 quotes) | Quotes of famous people

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Famous Judea Pearl Quotes

“The research questions that motivate most quantitative studies in the health, social and behavioral sciences are not statistical but causal in nature. For example, what is the efficacy of a given drug in a given population? Whether data can prove an employer guilty of hiring discrimination? What fraction of past crimes could have been avoided by a given policy? What was the cause of death of a given individual, in a specific incident? These are causal questions because they require some knowledge of the data-generating process; they cannot be computed from the data alone.”

Judea Pearl

Pearl, Judea (2008) "Causal Inference," in: Pearl, Judea. The science and ethics of causal modeling. (2010).

“An event recognised as responsible for the production of a certain outcome… Human intuition is extremely keen in detecting and ascertaining this type of causation and hence is considered the key to construct explanations… and the ultimate criterion (known as “cause in fact”) for determining legal responsibility.
Clearly, actual causation requires information beyond that of necessity and sufficiency: the actual process mediating between the cause and the effect must enter into consideration.”

Judea Pearl

Source: Causality: Models, Reasoning, and Inference, 2000, p. 309, as cited in: Rinke Hoekstra (2009), Ontology Representation: Design Patterns and Ontologies... p. 181

“A quantity Q(M) is identifiable, given a set of assumptions A, iffor any two models M1 and M2 that satisfy A, we have
P(M1) = P(M2) ⇒ Q(M1) = Q(M2)”

Judea Pearl

Source: Causality: Models, Reasoning, and Inference, 2000, p. 77 : cited in: Rick H. Hoyle (2014). Handbook of Structural Equation Modeling. p. 75
Hoyle (2014) further explained: In words, the functional details of M1 and M2 do not matter; what matters is that the assumptions in A (e.g. those encoded in the diagram) would constrain the variability of those details in such a way that equality of P's would entail equality of Q's. When this happens, Q depends on P only and should therefore be expressible in terms of the parameters of P. The section “Identification Using Graphs” will exemplify and operationalize this notion.

“Bayesian networks [is] a term coined in Pearl (1985) to emphasize three aspects: (1) the subjective nature of the input information; (2) the reliance on Bayes' conditioning as the basis of updating information; (3) the distinction between causal and evidential models of reasoning, a distinction that underscores Thomas Bayes' paper of 1763”

Judea Pearl

Source: Causality: Models, Reasoning, and Inference, 2000, p. 14

“Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon.”

Judea Pearl

Pearl, Judea. "Causal inference in statistics: An overview." Statistics Surveys 3 (2009): 96-146.

“Simpson's paradox (Simpson 1951; Blyth 1972), first encountered by Pearson in 1899 (Aldrich 1995), refers to the phenomenon whereby an event C increases the probability of E in a given population p and, at the same time, decreases the probability of E in every subpopulation of p.”

Judea Pearl

Source: Causality: Models, Reasoning, and Inference, 2000, p. 1

“Mr. Holmes receives a telephone call from his neighbor Dr. Watson who states that he hears the sound of a burglar alarm from the direction of of Mr. Holmes' house. While preparing to rush home, Mr. Holmes recalls that Dr. Watson is known to be a tasteless practical joker…”

Judea Pearl

Source: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988, p. 42: Example of an uncertain evidence