publication . Other literature type . Article . 1994

Discussion of ‘Causal diagrams for empirical research’ by J. Pearl

Judea Pearl;
  • Published: 01 Jan 1994
  • Publisher: Oxford University Press (OUP)
  • Country: United States
Abstract
SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can ...
Subjects
arXiv: Computer Science::Databases
ACM Computing Classification System: ComputerApplications_COMPUTERSINOTHERSYSTEMS
free text keywords: graph models, interventions treatment effect, Inference, Structural equation modeling, Nonparametric statistics, Econometrics, Language of mathematics, Expression (mathematics), Causal inference, Graphical model, Empirical research, Mathematics, Statistics, Agricultural and Biological Sciences (miscellaneous), Statistics, Probability and Uncertainty, Statistics and Probability, Applied Mathematics, General Agricultural and Biological Sciences, General Mathematics
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publication . Other literature type . Article . 1994

Discussion of ‘Causal diagrams for empirical research’ by J. Pearl

Judea Pearl;