An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model

YANG Jing; AN Ning; WANG Kunxia; WANG Aiguo; LI Lian

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 553-557

YANG Jing, AN Ning, WANG Kunxia, et al., “An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 553-557, 2013,

Citation:

YANG Jing, AN Ning, WANG Kunxia, et al., “An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 553-557, 2013,

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An Efficient Causal Structure Learning Algorithm Based on Recursive Simultaneous Equations Model

School of Computer and Information, Hefei University of Technology, Hefei 230009, China

Funds: This work is supported by the National High Technology Research and Development Program of China (863 Program) (No.2012AA011005), the National Natural Science Foundation of China (No.61175051, No.61070131, No.61175033), the Anhui Science and Technology Research Project Police Training Special Project (No.1001130612) and the Excellent Youth Personnel of Anhui Province (No.2012SQRL269).

Received Date: 2012-04-01
Rev Recd Date: 2012-12-01
Publish Date: 2013-06-15

Abstract

Abstract

A new algorithm, the RSEM (Recursive simultaneous equations model) algorithm, is presented for causal structure learning under the LSEM (Linear structural equations model). The algorithm effectively applies recursive simultaneous equations model to causal structure learning. This paper makes two specific contributions. Firstly, under the assumption that knowing the causal order of the variables, we show that recursive simultaneous equations model can be used for causal structure learning under the LSEM regardless of whether the datasets follow multivariate Gaussian distribution. Secondly, the performance of the RSEM algorithm is compared with the state-of-the-art algorithms on 7 networks. Simulation results show that the RSEM algorithm outperforms existing algorithms in terms of time performance, and has a quite high accuracy for thresholds 0.005 and 0.01.
- Causal structure learning,
- Linear structural equations model (LSEM),
- Recursive simultaneous equations model (RSEM),
- Bayesian network

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References(12)

References

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