Causal Inference in the Presence of Latent Variables and Selection Bias
Abstract
We show that there is a general, informative and reliable procedure for discovering causal relations when, for all the investigator knows, both latent variables and selection bias may be at work. Given information about conditional independence and dependence relations between measured variables, even when latent variables and selection bias may be present, there are sufficient conditions for reliably concluding that there is a causal path from one variable to another, and sufficient conditions for reliably concluding when no such causal path exists.
Cite
Text
Spirtes et al. "Causal Inference in the Presence of Latent Variables and Selection Bias." Conference on Uncertainty in Artificial Intelligence, 1995. doi:10.7551/mitpress/2006.003.0009Markdown
[Spirtes et al. "Causal Inference in the Presence of Latent Variables and Selection Bias." Conference on Uncertainty in Artificial Intelligence, 1995.](https://mlanthology.org/uai/1995/spirtes1995uai-causal/) doi:10.7551/mitpress/2006.003.0009BibTeX
@inproceedings{spirtes1995uai-causal,
title = {{Causal Inference in the Presence of Latent Variables and Selection Bias}},
author = {Spirtes, Peter and Meek, Christopher and Richardson, Thomas S.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1995},
pages = {499-506},
doi = {10.7551/mitpress/2006.003.0009},
url = {https://mlanthology.org/uai/1995/spirtes1995uai-causal/}
}