Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation

Richard Scheines
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:445-456, 1997.

Abstract

The statistical evidence for the detrimental effect of low level lead exposure on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics proved crucial in making the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD II, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a latent variable, on the measured IQ score of middle class suburban children.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-scheines97a, title = {Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation}, author = {Scheines, Richard}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {445--456}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/scheines97a/scheines97a.pdf}, url = {https://proceedings.mlr.press/r1/scheines97a.html}, abstract = {The statistical evidence for the detrimental effect of low level lead exposure on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics proved crucial in making the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD II, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a latent variable, on the measured IQ score of middle class suburban children.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation %A Richard Scheines %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-scheines97a %I PMLR %P 445--456 %U https://proceedings.mlr.press/r1/scheines97a.html %V R1 %X The statistical evidence for the detrimental effect of low level lead exposure on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics proved crucial in making the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD II, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a latent variable, on the measured IQ score of middle class suburban children. %Z Reissued by PMLR on 30 March 2021.
APA
Scheines, R.. (1997). Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:445-456 Available from https://proceedings.mlr.press/r1/scheines97a.html. Reissued by PMLR on 30 March 2021.

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