Causal discovery can be a powerful tool for investigating causality when a system can be observed but is inaccessible to experiments in practice. Despite this, it is rarely used in any scientific or medical fields. One of the major hurdles preventing the field of causal discovery from having a larger impact is that it is difficult to determine when the output of a causal discovery method can be trusted in a real-world setting. Trust is especially critical when human health is on the line. In this paper, we report the results of a series of simulation studies investigating the performance of different resampling methods as indicators of confidence in discovered graph features. We found that subsampling and sampling with replacement both performed surprisingly well, suggesting that they can serve as grounds for confidence in graph features. We also found that the calibration of subsampling and sampling with replacement had different convergence properties, suggesting that one's choice of which to use should depend on the sample size.
|Original language||English (US)|
|Title of host publication||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Editors||Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Nov 2019|
|Event||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States|
Duration: Nov 18 2019 → Nov 21 2019
|Name||Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Conference||2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019|
|Period||11/18/19 → 11/21/19|
Bibliographical noteFunding Information:
This work was supported by funding from NCRR 1UL1TR002494-01, 1R01MH116156-01A1, and 1R03MH117254-01A1 to EK.
© 2019 IEEE.
- Causal discovery
- graphical models