CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors
| dc.contributor.author | Adib, Riddhiman | |
| dc.contributor.author | Naved, Md Mobasshir Arshed | |
| dc.contributor.author | Fang, Chih-Hao | |
| dc.contributor.author | Gani, Md Osman | |
| dc.contributor.author | Grama, Ananth | |
| dc.contributor.author | Griffin, Paul | |
| dc.contributor.author | Ahamed, Sheikh Iqbal | |
| dc.contributor.author | Adibuzzaman, Mohammad | |
| dc.date.accessioned | 2022-06-10T15:22:55Z | |
| dc.date.available | 2022-06-10T15:22:55Z | |
| dc.date.issued | 2022-04-28 | |
| dc.description.abstract | Structural causal models (SCMs) provide a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. SCMs, however, require domain knowledge, which is typically represented as graphical models. A key challenge in this context is the absence of a methodological framework for encoding priors (background knowledge) into causal models in a systematic manner. We propose an abstraction called causal knowledge hierarchy (CKH) for encoding priors into causal models. Our approach is based on the foundation of "levels of evidence" in medicine, with a focus on confidence in causal information. Using CKH, we present a methodological framework for encoding causal priors from various data sources and combining them to derive an SCM. We evaluate our approach on a simulated dataset and demonstrate overall performance compared to the ground truth causal model with sensitivity analysis. | en_US |
| dc.description.uri | https://arxiv.org/abs/2204.13775 | en_US |
| dc.format.extent | 25 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | en_US |
| dc.identifier | doi:10.13016/m2yhjq-lccf | |
| dc.identifier.uri | https://doi.org/10.48550/arXiv.2204.13775 | |
| dc.identifier.uri | http://hdl.handle.net/11603/24888 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | en_US |
| dc.title | CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priors | en_US |
| dc.type | Text | en_US |