| Presentation | 2022-06-28 Causal Discovery in Discrete Data Using NML Code Length Based on MDL Principle Masatoshi Kobayashi, Nishimoto Hiroki, Shin Mastushima, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Inference on the causal structure among random variables from only a finite number of observed data is one of the most important problems in science. This paper introduces causal inference methods for discrete variable data using NML code lengths for multinomial distribution models based on the MDL principle and BIC. These methods take an approach in which the estimation of a four-way causal relationship between two variables is directly solved as a model selection problem. We show that this approach is an efficient and accurate causal discovery method for discrete variable pairs using synthetic data. Further, we observed that the model selection method using the NML code length can estimate causal relationships with higher accuracy. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Causal Discovery / MDL Principle / Stochastic Complexity / Discrete Data / BIC / ANMs |
| Paper # | NC2022-21,IBISML2022-21 |
| Date of Issue | 2022-06-20 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
|---|---|
| Conference Date | 2022/6/27(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hiroshi Yamakawa(Univ of Tokyo) / Masashi Sugiyama(Univ. of Tokyo) |
| Vice Chair | Hirokazu Tanaka(Tokyo City Univ.) / Toshihiro Kamishima(AIST) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Hirokazu Tanaka(NTT) / Toshihiro Kamishima(NICT) / Koji Tsuda(NTT) / (Hokkaido Univ.) |
| Assistant | Yoshimasa Tawatsuji(Waseda Univ.) / Tomoki Kurikawa(KMU) / Yoshinobu Kawahara(Osaka Univ.) / Taiji Suzuki(Tokyo Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Bioinformatics and Genomics / Special Interest Group on Mathematical Modeling and Problem Solving |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Causal Discovery in Discrete Data Using NML Code Length Based on MDL Principle |
| Sub Title (in English) | |
| Keyword(1) | Causal Discovery |
| Keyword(2) | MDL Principle |
| Keyword(3) | Stochastic Complexity |
| Keyword(4) | Discrete Data |
| Keyword(5) | BIC |
| Keyword(6) | ANMs |
| 1st Author's Name | Masatoshi Kobayashi |
| 1st Author's Affiliation | The University of Tokyo(Todai) |
| 2nd Author's Name | Nishimoto Hiroki |
| 2nd Author's Affiliation | The University of Tokyo(Todai) |
| 3rd Author's Name | Shin Mastushima |
| 3rd Author's Affiliation | The University of Tokyo(Todai) |
| Date | 2022-06-28 |
| Paper # | NC2022-21,IBISML2022-21 |
| Volume (vol) | vol.122 |
| Number (no) | NC-89,IBISML-90 |
| Page | pp.pp.149-155(NC), pp.149-155(IBISML), |
| #Pages | 7 |
| Date of Issue | 2022-06-20 (NC, IBISML) |