Estimating Latent Causal Inferences: Tetrad II Model Selection and Bayesian Parameter Estimation
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
Text
Scheines. "Estimating Latent Causal Inferences: Tetrad II Model Selection and Bayesian Parameter Estimation." Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997.Markdown
[Scheines. "Estimating Latent Causal Inferences: Tetrad II Model Selection and Bayesian Parameter Estimation." Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997.](https://mlanthology.org/aistats/1997/scheines1997aistats-estimating/)BibTeX
@inproceedings{scheines1997aistats-estimating,
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},
year = {1997},
pages = {445-456},
volume = {R1},
url = {https://mlanthology.org/aistats/1997/scheines1997aistats-estimating/}
}