Using Bayesian networks to identify the causal effect of speeding in individual vehicle/pedestrian collisions

Authors:
Gary A. Davis
Affiliations:
Department of Civil Engineering, University of Minnesota, Minneapolis, MN
Venue:
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Year:
2001

Citing 2
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Probabilistic evaluation of counterfactual queries

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference

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Abstract

Estimating individual probabilities of causation generally requires prior knowledge of causal mechanisms. For traffic accidents such knowledge is often available and supports the discipline of accident reconstruction. In this paper structural knowledge is combined with Bayesian network methods to calculate the probability of necessity due to speeding for each of a set of vehicle/pedestrian collisions. Gibbs sampling is used to carry out the computations.