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+# GFlowCausal
+
+## Paper
+
+GFlowCausal: Generative Flow Networks for Causal Discovery, NeurIPS 2022
+
+## Introduction
+
+Causal discovery aims to uncover causal structure among a set of variables. Score-based is one primary causal discovery class, which focuses on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose GFlowCausal to convert the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficiently sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting.
+
+## Framework
+
+![](http://image.huawei.com/tiny-lts/v1/images/89a9a4466d4e91f9ed683be59fa83673_1019x583.png)
+Our code will be uploaded here soon after the company review.
+
+## main function
+
+main_ms.py
+
+## Datasets
+
+ER銆丼F
+
+## Environment
+
+language锛歱ython 3.7.0
+
+framework锛歁indSpore 1.5.0
+
+## Directory
+
+```test
+.
+鈹斺攢GFlowCausal
+  |
+  鈹溾攢castle
+  | 鈹溾攢metrics
+  | 鈹溾攢datasets
+  | 鈹斺攢data_loader.py        # data
+  |
+  鈹溾攢networl
+  | 鈹溾攢model_ms.py          # ms network model
+  |
+  鈹溾攢README.md
+  鈹溾攢requirements.txt
+  鈹溾攢env.py                 # environment
+  鈹溾攢lossnetwork.py         # mindspore loss function
+  鈹溾攢args.py
+  鈹溾攢main_ms.py
+  鈹斺攢utils.py
+```