{"id":25965124,"url":"https://github.com/werkaaa/iscm","last_synced_at":"2026-03-06T08:01:22.211Z","repository":{"id":279836422,"uuid":"939681347","full_name":"werkaaa/iscm","owner":"werkaaa","description":"Standardizing Structural Causal Models, ICLR 2025","archived":false,"fork":false,"pushed_at":"2025-06-19T11:42:54.000Z","size":597,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-09-28T07:02:14.219Z","etag":null,"topics":["benchmarking","causal-discovery","causality","scm","structure-learning"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2406.11601","language":"Jupyter Notebook","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/werkaaa.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,"zenodo":null}},"created_at":"2025-02-26T23:41:20.000Z","updated_at":"2025-09-04T08:51:47.000Z","dependencies_parsed_at":"2025-02-28T01:24:18.842Z","dependency_job_id":"f16a5522-3c59-4d09-8af3-bd237a58a961","html_url":"https://github.com/werkaaa/iscm","commit_stats":null,"previous_names":["werkaaa/iscm"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/werkaaa/iscm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/werkaaa%2Fiscm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/werkaaa%2Fiscm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/werkaaa%2Fiscm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/werkaaa%2Fiscm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/werkaaa","download_url":"https://codeload.github.com/werkaaa/iscm/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/werkaaa%2Fiscm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30166849,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-06T07:56:45.623Z","status":"ssl_error","status_checked_at":"2026-03-06T07:55:55.621Z","response_time":250,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["benchmarking","causal-discovery","causality","scm","structure-learning"],"created_at":"2025-03-04T21:37:00.646Z","updated_at":"2026-03-06T08:01:22.203Z","avatar_url":"https://github.com/werkaaa.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Standardizing Structural Causal Models\n\n![PyPi](https://img.shields.io/pypi/v/iscm?logo=PyPI) [![DOI](https://zenodo.org/badge/939681347.svg)](https://doi.org/10.5281/zenodo.15697868)\n\n\nThis is the code repository for the paper *Standardizing Structural Causal Models*\n([Ormaniec et al., 2025](https://openreview.net/forum?id=aXuWowhIYt\u0026referrer=%5BAuthor%20Console%5D), ICLR 2025).\n\nComprehensive code for reproducing the results from the paper can be found in the\n[iscm_full](https://github.com/werkaaa/iscm/tree/iscm_full) branch. Here, we introduce the `iscm` library that packages\nsampling from iSCMs, SCMs, and naively standardized SCMs.\n\n## Library\n\nTo install the `iscm` library, run:\n```\npip install iscm\n```\n\nThe code snippet below shows how you can sample from an iSCM.\n```python\nimport numpy as np\n\nfrom iscm import data_sampler, graph_sampler\n\nrng = np.random.default_rng(seed=0)\n\n# Generate a graph\ngraph = graph_sampler.generate_erdos_renyi_graph(\n            num_nodes=20,\n            edges_per_node=2,\n            weight_range=(0.5, 2.0), # The weights will be sampled randomly from ± weight range\n            rng=rng,\n        )\n\n# Sample data\niscm_sample = data_sampler.sample_linear(\n                  graph=graph,\n                  sample_size=100,\n                  standardization='internal',\n                  rng=rng,\n              )\n```\n\nWe recommend using the functions in [graph_sampler.py](https://github.com/werkaaa/iscm/blob/main/iscm/graph_sampler.py) and [data_sampler.py](https://github.com/werkaaa/iscm/blob/main/iscm/data_sampler.py) to sample graphs and data.\nFor an overview of library functionalities, see [iSCM_Tutorial.ipynb](https://github.com/werkaaa/iscm/blob/main/iSCM_Tutorial.ipynb), which you can directly open in Google Colab:\n\n\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/werkaaa/iscm/blob/main/iSCM_Tutorial.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\n\n## Reference\n```\n@article{ormaniec2025standardizing,\n    title={Standardizing Structural Causal Models},\n    author={Weronika Ormaniec and Scott Sussex and Lars Lorch and Bernhard Sch{\\\"o}lkopf and Andreas Krause},\n    journal={The Thirteenth International Conference on Learning Representations},\n    year={2025}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwerkaaa%2Fiscm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwerkaaa%2Fiscm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwerkaaa%2Fiscm/lists"}