Using Artificial Intelligence to Aid in the Development of Causal Models

Abstract

Data analysis that merely fits an empirical covariance matrix or that finds the best least squares linear estimator of a variable is not a reliable guide to judgements about policy, which inevitably involve causal conclusions. We have developed and tested a computer program TETRAD II, that accepts as input background knowledge about a causal structure, a covariance matrix, and a sample size, and outputs a set of suggested models compatible with the background knowledge and that explain the data. In tests on simulated data, TETRAD II was able to suggest a set of models that included the correct one 94% of the time. We have also applied TETRAD II to several data sets supplied by the Naval Personnel Research and Development Center.

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Document Details

Document Type
Technical Report
Publication Date
Dec 06, 1990
Accession Number
ADA231273

Entities

People

  • Clark Glymour
  • Peter Spirtes
  • Richard Scheines
  • Steve Sorensen

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Information Science
  • Military Personnel
  • Military Research
  • Monte Carlo Method
  • Naval Personnel
  • Probability Distributions
  • Random Variables
  • Social Sciences
  • Statistical Algorithms
  • Statistical Analysis
  • Surveys

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Business Analytics
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference