## 🌐 Github URL [https://github.com/papamoon0113/Causal-Discovery-Python-Implement](https://github.com/papamoon0113/Causal-Discovery-Python-Implement) ## 📜 Read.me Hi, there! 👋 I’m a student studying Causal Discovery, and this page is created to share my implemented codes. All the codes here are implemented by using only *numpy, scipy, pandas*, and *matplotlib*. While studying Causal Discovery, I found that there were few proper code implementation examples online. Most of the code online used pre-existing package, such as networkx, dowhy, cdt, etc., which made it difficult to understand how the underlying algorithm **really** worked. (There is also a way to read the package code, but it is usually too complicated. 😓) As a result, I decided to implement the code myself based on research papers and books. I share my code for the people who prefer to study while implementing the code themselves, rather than relying on pre-existing package. I recommend to follow **Implementation Procedure** below. I hope that my work will be helpful for you. 😁 ### **I recommend it to people like this!** - **Who wants to understand Causal Discovery algorithms while implementing them** 👦 - **Who wants to implement their own algorithm by modifying pre-existing algorithms** 👩 ## Implementation Procedure ### 0. DAG Pattern ❗ **We don’t use networkx. We will implement our own graph class. Visualization, too** | Title | Implementation | Notebook url | | --- | --- | --- | | DAG Pattern_base | Foundation of DAG Pattern | [Click!](https://colab.research.google.com/drive/1cDxmQPL-v3egbrZVsklQDzbukHNVgeVq?usp=share_link) | | DAG Pattern_d_separation | Methods related to d-separation | [Click!](https://colab.research.google.com/drive/1GZ6lX4RzSzSJriIe5_t-tRYZd4YULxoF?usp=share_link) | | DAG Pattern_visualization | Visualization (Force-directed graph drawing) | [Click!](https://colab.research.google.com/drive/1hCDTh3zttekN6YFuvDiQpHd3YMrX7cIU?usp=share_link) | ### 1. Nonparametric CI Test | Title | Implementation | Notebook url | | --- | --- | --- | | Nonparametric CI Test_MI | Conditional Mutual Information | [Click!](https://colab.research.google.com/drive/1n-62Din_vq5TY9zFrjxnXvK4iM2XQw9j?usp=share_link) | | Nonparametric CI Test_KCIT | KCIT, Kernel-based Conditional Independence Test | [Click!](https://colab.research.google.com/drive/10Y37wFC4v3cl_7WNFheeUuH4iwKKREgq?usp=share_link) | ### 2. Constraint-based Algorithm | Title | Implementation | Notebook url | | --- | --- | --- | | Constraint-based Algorithm_basic | Basic algorithm of Constraint-based Algorithm | [Click!](https://colab.research.google.com/drive/1Rrpdw1IlPNKN_yVqc1ZSFx_fzKfHdDtD?usp=share_link) | | Constraint-based Algorithm_pc | PC algorithm | [Click!](https://colab.research.google.com/drive/1jhauXC8LsdViE8R58-9GuPRxdcNUUBC2?usp=share_link) | | Constraint-based Algorithm_cpc | Conservative PC algorithm | [Click!](https://colab.research.google.com/drive/1NZMTB-jG8AaOBFiRdxGOohn2Q7j5tq31?usp=share_link) | ## Reference [1] Geiger, D., Verma, T., & Pearl, J. (1990). d-Separation: From Theorems to Algorithms. In *Machine Intelligence and Pattern Recognition* (Vol. 10, Issue C) [2] Zhang, K., Peters, J., Janzing, D., & Schölkopf, B. (2011). Kernel-based conditional independence test and application in causal discovery. *Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011* [3] Metropolitan. (2004). Learning Bayesian Networks. chapter 10. [4] Verma, T., Pearl, J. (1990). On the Equivalence of Causal Models. *Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)*. [5] Meek, C. (1995). Causal inference and causal explanation with background knowledge. *Conference on Uncertainty in Artificial Intelligence.* [6] Ramsey, J., Spirtes, P., & Zhang, J. (2006). Adjacency-faithfulness and conservative causal inference. *Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006*. [7] Peter, J., Zanzing, D., Schölkopf, B. (2017). Elements of Causal Inference. *The MIT Press*