- An Empirical Study of one of the Simplest Causal Prediction Algorithms
- CEUR Workshop Proceedings
- Article number
- Pages (from-to)
- Document type
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
We study one of the simplest causal prediction algorithms that uses only conditional independences estimated from purely observational data. A specific pattern of four conditional independence relations amongst a quadruple of random variables already implies that one of these variables causes another one without any confounding. As a consequence, it is possible to predict what would happen under an intervention on that variable without actually performing the intervention. Although the method is asymptotically consistent and works well in settings with only few (latent) variables, we find that its prediction accuracy can be worse than simple (inconsistent) baselines when many (latent) variables are present. Our findings illustrate that violations of strong faithfulness become increasingly likely in the presence of many latent variables, and this can significantly deterioriate the accuracy of constraint-based causal prediction algorithms that assume faithfulness.
- Final publisher version
- Proceedings title: UAI2015-ACI : UAI 2015 Workshop on Advances in Causal Inference : proceedings of the UAI 2015 Workshop
on Advances in Causal Inference, co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015)
: Amsterdam, The Netherlands, July 16, 2015
Place of publication: Aachen
Editors: R. Silva, I. Shpitser, R. Evans, J. Peters, T. Claassen
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