因果发现工具箱

导语

Causal Discovery Toolbox （cdt）是一个应用于图模型和Pyhton >=3.5 版本中的因果推断工具箱。包含了图结构恢复和相关依赖。该工具箱基于以下模块和语言构建：Numpy、Sciki-learn、Pytorch、R。

该工具箱包含了很多基于观测数据进行图结构恢复的算法（诸如 bnlearn、pcalg 算法）。

Leo| 编译

邓一雪| 编辑

文档题目： Causal Discovery Toolbox Documentation ‒ Causal Discovery Toolbox 0.5.23 documentation 文档链接：： https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html

Docker 镜像

该工具箱提供了Docker镜像，镜像中包含了所有依赖和启动项。

安装

需要版本号不低于 3.5 的Pyhton，以及 requirements.txt 中所列举出的模块。如果需要使用额外的功能则需要安装更多的模块。在安装指南中可以看到最小安装和完全安装的方式。

注意：对于非专业用户而言，(mini/ana) conda 框架有助于管理相关依赖。请查阅官网 http://pytorch.org 获取相关的配置信息。

安装 PyTorch

由于cdt（CausalDiscoveryToolbox）模块模块中的关键算法使用了 PyTorch 模块，所以需要安装PyTorch。

通过 PyPi 安装 CausalDiscoveryToolbox

可通过PyPi安装相关模块：

pip install cdt

也可以通过源代码安装

$ git clone https://github.com/FenTechSolutions/CausalDiscoveryToolbox.git # Download the package

$ cd CausalDiscoveryToolbox

$ pip install -r requirements.txt # Install the requirements

$ python setup.py install develop --user

安装完成后就可以导入运行模块。CausalDiscoveryToolbox 中的大多数算法应该都可以使用，不可用的算法会看到警告。

可通过一下方式导入模块：

import cdt

文档中包含了该模块的额外信息：

https://github.com/FenTechSolutions/CausalDiscoveryToolbox/blob/master/documentation.md

R语言与R语言库

如果要在使用 cdt 模块中，使用诸如 nlearn、kpcalg、pcalg 等由 R 语言编写的的算法，就需要安装 R 语言。

travis.yml 文件的预安装部分可以查看基于 Debian 的R语言安装依赖。r-requirements 文件则包含了 cdt 模块所需要的 R 语言包。

文件总览

模块包结构

下图展现了相关的模块与算法包结构

硬件与算法设置

该工具箱有一个SETTING类可用于对硬件进行设置。这些设置是唯一的。默认参数定义于cdt/utils/Settings

可以通过一下代码访问并修改配置

import cdt

cdt.SETTINGS

此外在启动时，可以使用 cdt.utils.Settings.autoset_settings 方法来自动配置硬件参数（例如，GPU，CPU数量，可选软件包的数量）

图类

相关模块用到了两个基于 networkx 的类，分别是：DiGraph 和 Graph。

参考

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[11] Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11(Oct), 2837-2854.

[12] Goudet, O., Kalainathan, D., Caillou, P., Lopez-Paz, D., Guyon, I., Sebag, M., ... & Tubaro, P. (2017). Learning functional causal models with generative neural networks. arXiv preprint arXiv:1709.05321.

[13] Spirtes, P., Glymour, C., Scheines, R. (2000). Causation, Prediction, and Search. MIT press.

[14] Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2009). Nonlinear causal discovery with additive noise models. In Advances in neural information processing systems (pp. 689-696).

[15] Janzing, D., Mooij, J., Zhang, K., Lemeire, J., Zscheischler, J., Daniušis, P., ... & Schölkopf, B. (2012). Information-geometric approach to inferring causal directions. Artificial Intelligence, 182, 1-31.

[16] Lopez-Paz, D., Muandet, K., Schölkopf, B., & Tolstikhin, I. (2015, June). Towards a learning theory of cause-effect inference. In International Conference on Machine Learning (pp. 1452-1461).

[17] Lopez-Paz, D., Nishihara, R., Chintala, S., Schölkopf, B., & Bottou, L. (2017, July). Discovering causal signals in images. In Proceedings of CVPR.

[18] Stegle, O., Janzing, D., Zhang, K., Mooij, J. M., & Schölkopf, B. (2010). Probabilistic latent variable models for distinguishing between cause and effect. In Advances in Neural Information Processing Systems (pp. 1687-1695).

[19] Zhang, K., & Hyvärinen, A. (2009, June). On the identifiability of the post-nonlinear causal model. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (pp. 647-655). AUAI Press.

[20] Fonollosa, J. A. (2016). Conditional distribution variability measures for causality detection. arXiv preprint arXiv:1601.06680.

[21] Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723-773.

[22] Li, Y., Swersky, K., & Zemel, R. (2015). Generative moment matching networks. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15) (pp. 1718-1727).

[23] Margaritis D (2003). Learning Bayesian Network Model Structure from Data . Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA. Available as Technical Report CMU-CS-03-153

[24] Tsamardinos I, Aliferis CF, Statnikov A (2003). “Algorithms for Large Scale Markov Blanket Discovery”. In “Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference”, pp. 376-381. AAAI Press.

[25] Tsamardinos I, Aliferis CF, Statnikov A (2003). “Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations”. In “KDD ’03: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining”, pp. 673-678. ACM. Tsamardinos I, Brown LE, Aliferis CF (2006). “The Max-Min Hill-Climbing Bayesian Network Structure Learning Algorithm”. Machine Learning,65(1), 31-78.

[26] Kalainathan, Diviyan & Goudet, Olivier & Guyon, Isabelle & Lopez-Paz, David & Sebag, Michèle. (2018). SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning.

[27] Aragam, B., & Zhou, Q. (2015). Concave penalized estimation of sparse Gaussian Bayesian networks. Journal of Machine Learning Research, 16, 2273-2328.

[28] Bloebaum, P., Janzing, D., Washio, T., Shimizu, S., & Schoelkopf, B. (2018, March). Cause-Effect Inference by Comparing Regression Errors. In International Conference on Artificial Intelligence and Statistics (pp. 900-909).

[29] Structural Intervention Distance (SID) for Evaluating Causal Graphs, Jonas Peters, Peter Bühlmann: https://arxiv.org/abs/1306.1043

（参考文献可上下滑动查看）

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