Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:445-456, 1997.
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.