Judea Pearl: Processing

“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).

Death , Health , Behavior , Motivation

“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

“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.

Way , Mathematics , Knowledge , Strong

“When loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble.. If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium… Such oscillations do not normally occur in probabilistic networks… which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network.”

Judea Pearl

Source: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988, p. 195

Communication , Sense , Time