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Abstract
One of the common obstacles for learning causal models from data is that high-order conditional independence (CI) relationships between random variables are difficult to estimate. Since CI tests with conditioning sets of low order can be performed accurately even for a small number of observations, a reasonable approach to determine casual structures is to base merely on the low-order CIs. Recent research has confirmed that, e.g. in the case of sparse true causal models, structures learned even from zero- and first-order conditional independencies yield good approximations of the models. However, a challenging task here is to provide methods that faithfully explain a given set of low-order CIs. In this paper, we propose an algorithm which, for a given set of conditional independencies of order less or equal to k, where k is a small fixed number, computes a faithful graphical representation of the given set. Our results complete and generalize the previous work on learning from pairwise marginal independencies. Moreover, they enable to improve upon the 0-1 graph model which, e.g. is heavily used in the estimation of genome networks.
Original language | English |
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Pages | 10302-10309 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 03.04.2020 |
Event | The Thirty-Fourth AAAI Conference on Artificial Intelligence - New York, United States Duration: 07.02.2020 → 12.02.2020 |
Conference
Conference | The Thirty-Fourth AAAI Conference on Artificial Intelligence |
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Abbreviated title | AAAI-20 |
Country/Territory | United States |
City | New York |
Period | 07.02.20 → 12.02.20 |
DFG Research Classification Scheme
- 4.43-01 Theoretical Computer Science
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Dive into the research topics of 'Recovering Causal Structures from Low-Order Conditional Independencies'. Together they form a unique fingerprint.Projects
- 1 Finished
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Causality: an algorithmic framework and a computational complexity perspective
Liskiewicz, M. (Principal Investigator (PI)) & Textor, J. (Principal Investigator (PI))
01.01.16 → 31.12.22
Project: DFG Projects › DFG Individual Projects