Causal Discovery in the Presence of Measurement Error

Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, PMLR R16:569-578, 2018.

Abstract

Causal discovery algorithms infer causal re- lations from data based on several assump- tions, including notably the absence of mea- surement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bound for the variance of random mea- surement error from the covariance matrix of measured variables and how to use this up- per bound as a correction for constraint-based causal discovery. We demonstrate a practical application of our approach on both simulated data and real-world protein signaling data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR16-blom18a, title = {Causal Discovery in the Presence of Measurement Error}, author = {Blom, Tineke and Klimovskaia, Anna and Magliacane, Sara and Mooij, Joris M.}, booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence}, pages = {569--578}, year = {2018}, editor = {Globerson, Amir and Silva, Ricardo}, volume = {R16}, series = {Proceedings of Machine Learning Research}, month = {06--10 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r16/main/assets/blom18a/blom18a.pdf}, url = {https://proceedings.mlr.press/r16/blom18a.html}, abstract = {Causal discovery algorithms infer causal re- lations from data based on several assump- tions, including notably the absence of mea- surement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bound for the variance of random mea- surement error from the covariance matrix of measured variables and how to use this up- per bound as a correction for constraint-based causal discovery. We demonstrate a practical application of our approach on both simulated data and real-world protein signaling data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Causal Discovery in the Presence of Measurement Error %A Tineke Blom %A Anna Klimovskaia %A Sara Magliacane %A Joris M. Mooij %B Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2018 %E Amir Globerson %E Ricardo Silva %F pmlr-vR16-blom18a %I PMLR %P 569--578 %U https://proceedings.mlr.press/r16/blom18a.html %V R16 %X Causal discovery algorithms infer causal re- lations from data based on several assump- tions, including notably the absence of mea- surement error. However, this assumption is most likely violated in practical applications, which may result in erroneous, irreproducible results. In this work we show how to obtain an upper bound for the variance of random mea- surement error from the covariance matrix of measured variables and how to use this up- per bound as a correction for constraint-based causal discovery. We demonstrate a practical application of our approach on both simulated data and real-world protein signaling data. %Z Reissued by PMLR on 04 October 2026.
APA
Blom, T., Klimovskaia, A., Magliacane, S. & Mooij, J.M.. (2018). Causal Discovery in the Presence of Measurement Error. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R16:569-578 Available from https://proceedings.mlr.press/r16/blom18a.html. Reissued by PMLR on 04 October 2026.

Related Material