{"id":13541020,"url":"https://github.com/rguo12/awesome-causality-data","last_synced_at":"2025-04-02T08:30:54.933Z","repository":{"id":44454332,"uuid":"145769539","full_name":"rguo12/awesome-causality-data","owner":"rguo12","description":"A data index for learning causality.","archived":false,"fork":false,"pushed_at":"2023-10-25T12:59:45.000Z","size":32,"stargazers_count":413,"open_issues_count":0,"forks_count":65,"subscribers_count":13,"default_branch":"master","last_synced_at":"2024-05-21T02:24:14.358Z","etag":null,"topics":[],"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/rguo12.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}},"created_at":"2018-08-22T22:28:21.000Z","updated_at":"2024-05-08T07:49:32.000Z","dependencies_parsed_at":"2024-02-03T19:51:08.374Z","dependency_job_id":null,"html_url":"https://github.com/rguo12/awesome-causality-data","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/rguo12%2Fawesome-causality-data","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rguo12%2Fawesome-causality-data/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rguo12%2Fawesome-causality-data/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rguo12%2Fawesome-causality-data/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rguo12","download_url":"https://codeload.github.com/rguo12/awesome-causality-data/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246781901,"owners_count":20832931,"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":[],"created_at":"2024-08-01T10:00:37.515Z","updated_at":"2025-04-02T08:30:54.676Z","avatar_url":"https://github.com/rguo12.png","language":null,"funding_links":[],"categories":["Other Awesome List","Other Awesome lists"],"sub_categories":["Causal Discovery"],"readme":"# awesome-causality-data\nAn index of datasets that can be used for learning causality.\n\nPlease cite our survey if this data index helps your research.\n\n```\n@article{guo2018survey,\n  title={A Survey of Learning Causality with Data: Problems and Methods},\n  author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P. Richard and Liu, Huan}, \n  journal={arXiv preprint arXiv:1809.09337}, \n  year={2018}\n}\n```\n\n*Updates coming soon* \n\n## Datasets for Learning Causal Effects (Causal Inference)\n\n### Causal Effect Estimation with Single Cause\n\n#### Datasets with i.i.d. samples\nStandard datasets for learning causal effects comes with each instance in the format of (**x**,d,y).\n\n[IHDP1](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/IHDP)\n\n[How is IHDP1 (setting A) simulated](https://github.com/vdorie/npci/tree/master/examples/ihdp_sim)\n\n[IHDP2](https://math.la.asu.edu/~prhahn/)\n\n[Twins](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/TWINS)\n\n[Job Training](http://users.nber.org/~rdehejia/data/nswdata2.html) ([Lalonde 1986 in the R package qte](https://rdrr.io/cran/qte/man/lalonde.html#heading-0))\n\n[ACIC Benchmark](https://github.com/vdorie/aciccomp/tree/master/2016)\n\n[News](https://github.com/d909b/perfect_match/tree/master/perfect_match/data_access/news)\n\n[TCGA](https://github.com/d909b/perfect_match/tree/master/perfect_match/data_access/tcga)\n\n[NLSM](https://github.com/grf-labs/grf/tree/master/experiments/acic18)\n\n#### Datasets with non-i.i.d. samples (with interference, spillover effect or auxiliary network information)\n\n[Amazon](https://drive.google.com/drive/u/1/folders/1Ff_GdfjhrDFbZiRW0z81lGJW-cUrYmo1)\n\n#### Datasets with instrumental Variables (IV)\nStandard datasets for learning causal effects, each instance has the format of (i,**x**,d,y).\n\n[1980 Census Extract](https://economics.mit.edu/faculty/angrist/data1/data/angkru95)\n\n[CPS Extract](https://economics.mit.edu/faculty/angrist/data1/data/angkru95)\n\n#### Datasets for Regression Discontinuity Design\n\n[Population Threshold RDD Datasets](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/PGXO5O)\n\n\n### Datasets with Multiple Causes\n\n\n## Datasets for Learning Causal Relationships (Causal Discovery)\n\n#### Distinguishing Cause from Effect\n[Database with cause-effect pairs (Tbingen  Cause-Effect  Pairs)](http://webdav.tuebingen.mpg.de/cause-effect/)\n\n[AntiCD3/CD28](https://science.sciencemag.org/content/308/5721/523)\n\n[Pittsburgh Bridges](http://archive.ics.uci.edu/ml)\n\n[Abalone](http://archive.ics.uci.edu/ml)\n\n\n#### Causal Bayesian Network\n[Lung Cancer Simple Set (LUCAS)](http://www.causality.inf.ethz.ch/data/LUCAS.html)\n\n## Datasets for Connections to Machine Learning\n### Datasets with randomized test set for recommendation systems\n|Name|Paper|URL|\n|---|---|---|\n|Coat|[Schnabel, Tobias, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. \"Recommendations as treatments: Debiasing learning and evaluation.\" arXiv preprint arXiv:1602.05352 (2016).](http://www.jmlr.org/proceedings/papers/v48/schnabel16.pdf)|[download](http://www.cs.cornell.edu/~schnabts/mnar/index.html)|\n|Yahoo! R3|[Schnabel, Tobias, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. \"Recommendations as treatments: Debiasing learning and evaluation.\" arXiv preprint arXiv:1602.05352 (2016).](http://www.jmlr.org/proceedings/papers/v48/schnabel16.pdf)|[download](https://webscope.sandbox.yahoo.com/catalog.php?datatype=r)|\n|Spotify Music Streaming Sessions|[Brost, Brian, Rishabh Mehrotra, and Tristan Jehan. \"The Music Streaming Sessions Dataset.\" In The World Wide Web Conference, pp. 2594-2600. ACM, 2019.](https://arxiv.org/pdf/1901.09851)|[download](https://www.spotify.com/)|\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frguo12%2Fawesome-causality-data","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frguo12%2Fawesome-causality-data","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frguo12%2Fawesome-causality-data/lists"}