Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings
The development of data science has been needed in environmental fields such as marine, weather, and soil data. In general, the datasets are large in some cases, but they are often small because they contain observation data that the analyses themselves are limited. In such a case, the data are stat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123005241 |
| _version_ | 1827389287503495168 |
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| author | Atsushi Kurotani Hirokuni Miyamoto Jun Kikuchi |
| author_facet | Atsushi Kurotani Hirokuni Miyamoto Jun Kikuchi |
| author_sort | Atsushi Kurotani |
| collection | DOAJ |
| description | The development of data science has been needed in environmental fields such as marine, weather, and soil data. In general, the datasets are large in some cases, but they are often small because they contain observation data that the analyses themselves are limited. In such a case, the data are statistically evaluated by increasing or decreasing the levels of factors using differential analysis, resulting in the essential factors are estimated. However, there is no consistent approach to the means of assessing strong associations as a group between factors. Causal inference method has the possibility to output effective results for small data, and the results are expected to provide important information for understanding the potential highly association between factors, not necessarily the inference with big data. Here, we describe essential checkpoints and settings for the calculation by a direct method for learning a linear non-Gaussian structural equation model (DirectLiNGAM) and validation methods for the calculation results by using DirectLiNGAM with small-scale model data as an additional discussion of DirectLiNGAM portion of the related research article. Thus, this study provides the statistical validation methods for the association networks, treatments, and interventions for structural inference as a group of essential factors. • Causal inference with DirectLiNGAM • Validation of correlation coefficient and feature importance • Validation using causal effect object and propensity scores |
| first_indexed | 2024-03-08T16:32:26Z |
| format | Article |
| id | doaj.art-92236e4041d64ede9fb340c67a2c0a7a |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-08T16:32:26Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-92236e4041d64ede9fb340c67a2c0a7a2024-01-06T04:38:58ZengElsevierMethodsX2215-01612024-06-0112102528Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settingsAtsushi Kurotani0Hirokuni Miyamoto1Jun Kikuchi2Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-0856, Japan; Tokyo University of Agriculture and Technology, Koganei, Tokyo 184-0012, JapanGraduate School of Horticulture, Chiba University: Matsudo, Chiba 271-8501, Japan; RIKEN Center for Integrated Medical Science, Yokohama, Kanagawa 230-0045, Japan; Corresponding author.RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, JapanThe development of data science has been needed in environmental fields such as marine, weather, and soil data. In general, the datasets are large in some cases, but they are often small because they contain observation data that the analyses themselves are limited. In such a case, the data are statistically evaluated by increasing or decreasing the levels of factors using differential analysis, resulting in the essential factors are estimated. However, there is no consistent approach to the means of assessing strong associations as a group between factors. Causal inference method has the possibility to output effective results for small data, and the results are expected to provide important information for understanding the potential highly association between factors, not necessarily the inference with big data. Here, we describe essential checkpoints and settings for the calculation by a direct method for learning a linear non-Gaussian structural equation model (DirectLiNGAM) and validation methods for the calculation results by using DirectLiNGAM with small-scale model data as an additional discussion of DirectLiNGAM portion of the related research article. Thus, this study provides the statistical validation methods for the association networks, treatments, and interventions for structural inference as a group of essential factors. • Causal inference with DirectLiNGAM • Validation of correlation coefficient and feature importance • Validation using causal effect object and propensity scoreshttp://www.sciencedirect.com/science/article/pii/S2215016123005241DirectLiNGAM: A causal inference by direct estimation approach for learning the basic LiNGAM model with non-Gaussian data |
| spellingShingle | Atsushi Kurotani Hirokuni Miyamoto Jun Kikuchi Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings MethodsX DirectLiNGAM: A causal inference by direct estimation approach for learning the basic LiNGAM model with non-Gaussian data |
| title | Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings |
| title_full | Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings |
| title_fullStr | Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings |
| title_full_unstemmed | Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings |
| title_short | Validation of causal inference data using DirectLiNGAM in an environmental small-scale model and calculation settings |
| title_sort | validation of causal inference data using directlingam in an environmental small scale model and calculation settings |
| topic | DirectLiNGAM: A causal inference by direct estimation approach for learning the basic LiNGAM model with non-Gaussian data |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123005241 |
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