{"id":15550104,"url":"https://github.com/hscspring/bayes-graph-and-causal-inference","last_synced_at":"2026-03-18T18:57:51.750Z","repository":{"id":105866814,"uuid":"189386390","full_name":"hscspring/Bayes-Graph-and-Causal-Inference","owner":"hscspring","description":"Graph based Bayes causal inference.","archived":false,"fork":false,"pushed_at":"2019-07-02T07:47:16.000Z","size":5,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-03T00:34:55.685Z","etag":null,"topics":["bayesian-inference","bayesian-network","causal-inference"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hscspring.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-05-30T09:36:18.000Z","updated_at":"2022-09-02T00:51:47.000Z","dependencies_parsed_at":null,"dependency_job_id":"6ab051a3-0745-4fe9-9c59-7c13a736cf53","html_url":"https://github.com/hscspring/Bayes-Graph-and-Causal-Inference","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FBayes-Graph-and-Causal-Inference","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FBayes-Graph-and-Causal-Inference/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FBayes-Graph-and-Causal-Inference/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FBayes-Graph-and-Causal-Inference/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hscspring","download_url":"https://codeload.github.com/hscspring/Bayes-Graph-and-Causal-Inference/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239188685,"owners_count":19597032,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","bayesian-network","causal-inference"],"created_at":"2024-10-02T13:50:16.898Z","updated_at":"2026-03-18T18:57:51.720Z","avatar_url":"https://github.com/hscspring.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Bayes-Graph-and-Causal-Inference\n\n## Study\n\n- [bayesgroup/deepbayes-2018: Seminars DeepBayes Summer School 2018](https://github.com/bayesgroup/deepbayes-2018)\n- [CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\n- [MIT Computational Cognitive Science Group - Resources](http://cocosci.mit.edu/resources)\n- [Directed GMs: Bayesian Networks](http://www.cs.cmu.edu/~epxing/Class/10708/lectures/lecture2-BNrepresentation.pdf)\n- [A Tutorial on Inference and Learning in Bayesian Networks](http://www.ee.columbia.edu/~vittorio/Lecture12.pdf)\n- [Bayesian networks](https://courses.cs.washington.edu/courses/cse515/09sp/slides/bnets.pdf)\n- [10708 Probabilistic Graphical Models](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html)\n- [Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public Health](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n\n## Package\n\n- [jmschrei/pomegranate: Fast, flexible and easy to use probabilistic modelling in Python.](https://github.com/jmschrei/pomegranate)\n- [deepmind/graph_nets: Build Graph Nets in Tensorflow](https://github.com/deepmind/graph_nets)\n- [thu-ml/zhusuan: A Library for Bayesian Deep Learning, Generative Models, Based on Tensorflow](https://github.com/thu-ml/zhusuan)\n- [AI-DI/Brancher: A user-centered Python package for differentiable probabilistic inference](https://github.com/AI-DI/Brancher)\n- [microsoft/dowhy: DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.](https://github.com/Microsoft/dowhy)\n- [pytorch/botorch: Bayesian optimization in PyTorch](https://github.com/pytorch/botorch)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhscspring%2Fbayes-graph-and-causal-inference","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhscspring%2Fbayes-graph-and-causal-inference","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhscspring%2Fbayes-graph-and-causal-inference/lists"}