{"id":13427578,"url":"https://github.com/napsternxg/awesome-causality","last_synced_at":"2026-01-25T09:35:28.899Z","repository":{"id":38095880,"uuid":"175134743","full_name":"napsternxg/awesome-causality","owner":"napsternxg","description":"Resources related to causality","archived":false,"fork":false,"pushed_at":"2024-02-19T02:42:47.000Z","size":101,"stargazers_count":267,"open_issues_count":2,"forks_count":38,"subscribers_count":20,"default_branch":"master","last_synced_at":"2026-01-21T16:14:37.170Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://shubhanshu.com/awesome-causality/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/napsternxg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2019-03-12T04:22:13.000Z","updated_at":"2026-01-15T12:30:58.000Z","dependencies_parsed_at":"2024-01-14T12:17:59.574Z","dependency_job_id":"2bc6b2d2-488a-485b-8ea4-be46db5971ba","html_url":"https://github.com/napsternxg/awesome-causality","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/napsternxg/awesome-causality","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/napsternxg%2Fawesome-causality","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/napsternxg%2Fawesome-causality/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/napsternxg%2Fawesome-causality/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/napsternxg%2Fawesome-causality/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/napsternxg","download_url":"https://codeload.github.com/napsternxg/awesome-causality/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/napsternxg%2Fawesome-causality/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28750875,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-25T09:00:19.176Z","status":"ssl_error","status_checked_at":"2026-01-25T09:00:04.131Z","response_time":113,"last_error":"SSL_read: 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":[],"created_at":"2024-07-31T01:00:32.265Z","updated_at":"2026-01-25T09:35:28.884Z","avatar_url":"https://github.com/napsternxg.png","language":null,"funding_links":[],"categories":["Topics","Related Repo","Relevant Awesome Lists","Causality","Others","Related Repos","Other Lists","Other Awesome List","No-Code Software"],"sub_categories":["Arxiv","Tutorials","Research Paper","TeX Lists","Causal Discovery","Relevant Awesome Lists"],"readme":"# Awesome Causality\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication](https://licensebuttons.net/l/zero/1.0/88x31.png)](https://creativecommons.org/publicdomain/zero/1.0/)\n\nResources related to causality.\nThis awesome list is different from other lists as it tries to compile major resources related to causality in one place under different categories.\n\n**NOTE:** This awesome list is still new and under development. Please feel free to contribute, before it can become worth sharing.\n\n- [Awesome Causality](#awesome-causality)\n  * [Other Awesome lists](#other-awesome-lists)\n- [Data](#data)\n- [Tools](#tools)\n- [Learning resources](#learning-resources)\n  * [Tutorials](#tutorials)\n  * [Blogs](#blogs)\n  * [Books](#books)\n  * [Courses](#courses)\n  * [Videos](#videos)\n- [Events](#events)\n  * [Workshops](#workshops)\n- [Communities, and Mailing lists](#communities--and-mailing-lists)\n- [Miscellaneous](#miscellaneous)\n\n\u003csmall\u003e\u003ci\u003e\u003ca href='http://ecotrust-canada.github.io/markdown-toc/'\u003eTable of contents generated with markdown-toc\u003c/a\u003e\u003c/i\u003e\u003c/small\u003e\n\n## Other Awesome lists\nThese list contain a more focused compilation of algorithms and data related to causality under more specific categories.\n\n* [awesome-causality-algorithms](https://github.com/rguo12/awesome-causality-algorithms)\n* [awesome-causality-data](https://github.com/rguo12/awesome-causality-data)\n\n# Data\n* [Amazon Review Sales](https://github.com/rguo12/CIKM18-LCVA) - [Google drive](https://drive.google.com/drive/u/1/folders/1Ff_GdfjhrDFbZiRW0z81lGJW-cUrYmo1) - [Paper](https://arxiv.org/abs/1808.03333)\n* [Jobs Training](http://users.nber.org/~rdehejia/data/nswdata2.html) - [Train](http://www.fredjo.com/files/jobs_DW_bin.train.npz) [Test](http://www.fredjo.com/files/jobs_DW_bin.test.npz) - [Paper](http://proceedings.mlr.press/v70/shalit17a.html)\n* [Twins](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/TWINS)\n* [Synthetic IHDP](https://github.com/AMLab-Amsterdam/CEVAE/tree/master/datasets/IHDP)\n* [2016 Atlantic Causal Inference competition](https://github.com/vdorie/aciccomp/tree/master/2016)\n* [News trearment effect measurement](http://www.fredjo.com/files/NEWS_csv.zip)\n* [Cause effect pairs](http://webdav.tuebingen.mpg.de/cause-effect/)\n* [Movie recommendations - Missing not at random (MNAR)](http://www.cs.cornell.edu/~schnabts/mnar/index.html) - [Paper](http://proceedings.mlr.press/v48/schnabel16.html)\n* [CHALEARN Fast Causation Coefficient Challenge](http://www.causality.inf.ethz.ch/cause-effect.php?page=rules) - [Kaggle](https://www.kaggle.com/c/cause-effect-pairs#description)\n* [Causal inference datasets in quantitative social sciences](https://github.com/kosukeimai/qss)\n\n# Tools\n* [Omega: Causal, Higher-Order, Probabilistic Programming](http://www.zenna.org/Omega.jl/latest/) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://julialang.org/assets/infra/logo.svg\" /\u003e\n* [Pyro: A probabilistic programming language built on PyTorch that contains the do() operator](https://pyro.ai) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [Whittemore: Causal Programming in Clojure](https://github.com/jtcbrule/whittemore) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://julialang.org/assets/infra/logo.svg\" /\u003e\n* [causaleffect: Functions for identification and transportation of causal effects](https://www.rdocumentation.org/packages/causaleffect) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [pgmpy: Probabilistic Graphical Models in python, extended to causal queries](https://pgmpy.org/inference.html#causal-inference) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [pyagrum: a GRaphical Universal Modeler with causal examples from the Book of Why](https://agrum.gitlab.io/pages/pyagrum.html) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [Counterfactual regression](https://github.com/clinicalml/cfrnet) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [DoWhy - Microsoft Research](https://github.com/Microsoft/dowhy) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [Quantitative Social Science](https://github.com/kosukeimai/qss-package) - [Book](https://github.com/kosukeimai/qss) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [Causal Inference using Bayesian Additive Regression Trees](https://github.com/vdorie/bartCause) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [Non-parametrics for Causal Inference](https://github.com/vdorie/npci) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [Causality by author of Causal Data Science Series (see blogs)](https://github.com/akelleh/causality) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [InvariantCausalPrediction: Invariant Causal Prediction](https://cran.r-project.org/web/packages/InvariantCausalPrediction/index.html) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [Causal Discovery Toolbox](https://github.com/FenTechSolutions/CausalDiscoveryToolbox) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [CausalImpact - causal inference in time series](https://google.github.io/CausalImpact/) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [Daggity - Create causal graphs](http://www.dagitty.net/) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/r.svg\" /\u003e\n* [TETRAD](http://www.phil.cmu.edu/projects/tetrad/) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/java.svg\" /\u003e\n* [ProbLog - Do-calculus](https://dtai.cs.kuleuven.be/problog/tutorial/various/14_robot_key.html) \u003cimg height=\"16\" width=\"16\" color=\"blue\" src=\"https://unpkg.com/simple-icons@latest/icons/python.svg\" /\u003e\n* [Causalnex - A toolkit for causal reasoning with Bayesian Networks](https://github.com/quantumblacklabs/causalnex)\n* [Causal Fusion - A web based app for drawing and making inference via do-calculus on causal diagrams](https://causalfusion.net/app)\n* [DiCE - Generate Diverse Counterfactual Explanations for any machine learning model](https://github.com/interpretml/dice)\n* [CCD Causal Software suite](https://www.ccd.pitt.edu/tools/)\n* [The TETRAD Project - searching for causal explanations of data](https://github.com/cmu-phil/tetrad)\n* [Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML](https://github.com/uber/causalml)\n* [Causality Lab - research code of novel causal discovery algorithms developed at Intel Labs](https://github.com/IntelLabs/causality-lab)\n\n# Learning resources\n\n## Tutorials\n\n* [ICML 2016 Tutorial Causal Inference for Observational Studies](https://cs.nyu.edu/~shalit/tutorial.html)\n* [KDD 2018 Causal Inference Tutorial](https://causalinference.gitlab.io/kdd-tutorial/)\n* [Joris Mooij ML2 Causality](https://web.archive.org/web/20190312053009/https://drive.google.com/file/d/0B2DZf1QHTotxX2RiNXJ0NUwwekk/edit)\n* [Emre Kiciman - Observational Studies in Social Media (OSSM) at ICWSM 2017](https://web.archive.org/web/20180830204832/http://kiciman.org/wp-content/uploads/2016/06/tutorial_kiciman_ossm17.pdf)\n* [The Blessings of Multiple Causes: A Tutorial](https://github.com/blei-lab/deconfounder_tutorial)\n* [Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial)](https://www.youtube.com/watch?v=yKs6msnw9m8) - [Slides](https://web.archive.org/web/20181214003957/https://media.neurips.cc/Conferences/NIPS2018/Slides/Counterfactual_Inference.pdf)\n* [Ferenc Huszár Causal Inference Practical from MLSS Africa 2019](https://colab.research.google.com/drive/1rjjjA7teiZVHJCMTVD8KlZNu3EjS7Dmu#scrollTo=h2zDcSPqYuAa) - [\\[Notebook Runthrough\\]](https://www.youtube.com/watch?v=evmGGusk6gg) [\\[Video 1\\]](https://www.youtube.com/watch?v=HOgx_SBBzn0) [\\[Video 2\\]](https://www.youtube.com/watch?v=_RtxTpOb8e4)\n* [Causality notes and implementation in Python using statsmodels and networkX](https://github.com/ericmjl/causality)\n* [Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data](https://journals.sagepub.com/doi/10.1177/2515245917745629)\n* [The Hitchhiker’s Guide to the tlverse or a Targeted Learning Practitioner’s Handbook](https://tlverse.org/tlverse-handbook/)\n* [Causal Inference for The Brave and True](https://github.com/matheusfacure/python-causality-handbook)\n\n## Blogs, and Articles\n\n* [Causal Data Science Series](https://medium.com/causal-data-science/causal-data-science-721ed63a4027)\n* [Ferenc Huszár Series on Causal Modelling: various parts](https://www.inference.vc/) - [1](https://www.inference.vc/untitled/), [2](https://www.inference.vc/blessings-of-multiple-causes-causal-inference-when-you-cant-measure-confounders/), [3](https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/), [4](https://www.inference.vc/causal-inference-3-counterfactuals/)\n* [Diving deeper into causality Pearl, Kleinberg, Hill and untested assumptions](https://yanirseroussi.com/2016/05/15/diving-deeper-into-causality-pearl-kleinberg-hill-and-untested-assumptions/)\n* [Simpson's Paradox: An Anatomy](http://bayes.cs.ucla.edu/R264.pdf)\n* [Simpson’s paradox and causal inference with observational data](https://roamanalytics.com/2017/09/08/simpsons-paradox-and-causal-inference-with-observational-data/)\n* [Causation and Correlation - Talks about possible causes for observed correlations](https://kunalmenda.com/2019/02/21/causation-and-correlation/)\n* [(Non-)Identification in Latent Confounder Models](http://www.alexdamour.com/blog/public/2018/05/18/non-identification-in-latent-confounder-models/)\n* [Causal Inference Animated Plots - Good explanation of various causal inference methods](http://nickchk.com/causalgraphs.html)\n* [Explanation, prediction, and causality: Three sides of the same coin?](https://osf.io/u6vz5/)\n* [A chill intro to causal inference via propensity scores](https://osf.io/preprints/socarxiv/ncvqs/)\n* [All the DAGs from Hernan and Robins' Causal Inference Book by Sam Finlayson](https://sgfin.github.io/2019/06/19/Causal-Inference-Book-All-DAGs/) - [Causal Inference Book Part I -- Glossary and Notes](https://sgfin.github.io/2019/06/19/Causal-Inference-Book-Glossary-and-Notes/)\n* [Causal Inference with Bayes Rule by Gradient Institute](https://gradientinstitute.org/blog/6/)\n* [Causal Inference cheat sheet for data scientists](http://nc233.com/2020/04/causal-inference-cheat-sheet-for-data-scientists/)\n* [Which causal inference book you should read](https://www.bradyneal.com/which-causal-inference-book)\n* [Tweetorial on going from regression to estimating causal effects with machine learning](https://twitter.com/WomenInStat/status/1321595413573464064)\n* [Causal Inference in AI Education: A Primer](https://ftp.cs.ucla.edu/pub/stat_ser/r509.pdf) - Accompanying Tool [Learn.CI](https://learn.ci/)\n* [The Effect: An Introduction to Research Design and Causality](https://www.theeffectbook.net/index.html)\n* [What is Causal Inference and How Does It Work?](https://freecontent.manning.com/what-is-causal-inference-and-how-does-it-work/)\n\n## Books\n\n* [Causal Inference Book](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)\n* [Causal Inference in statistics: A primer](http://bayes.cs.ucla.edu/PRIMER/)\n* [Elements of Causal Inference - Foundations and Learning Algorithms (includes code examples in R and Jupyter notebooks)](http://web.math.ku.dk/~peters/elements.html)\n* [The Book of Why: The New Science of Cause and Effect](http://bayes.cs.ucla.edu/WHY/)\n* [Causal Inference Mixtape](http://scunning.com/mixtape.html) - [[R code](https://github.com/scunning1975/mixtape_learnr)] [[Python code](https://github.com/tomcaputo/mixtape_learnr/tree/main/Python)]\n* [Elements of Causal Inference - Foundations and Learning Algorithms](https://mitpress.mit.edu/books/elements-causal-inference)\n* [Actual Causality By Joseph Y. Halpern](https://www.cs.cornell.edu/home/halpern/papers/causalitybook-ch1-3.html)\n* [Causal Reasoning: Fundamentals and Machine Learning Applications by Emre Kiciman and Amit Sharma](https://causalinference.gitlab.io/)\n* [The Effect: An Introduction to Research Design and Causality](https://nickchk.com/causalitybook.html)\n* [Causal Inference for The Brave and True](https://matheusfacure.github.io/python-causality-handbook/)\n* [Bayesuvius: a visual dictionary of Bayesian Networks and Causal Inference](https://www.ar-tiste.com/bayesuvius.html) - [github](https://github.com/rrtucci/Bayesuvius/)\n* [Causal Inference for Data Science](https://www.manning.com/books/causal-inference-for-data-science) - [github](https://github.com/aleixrvr/CausalInference4DataScience)\n* [Causal Machine Learning](https://www.manning.com/books/causal-machine-learning) - [github](https://github.com/robertness)\n\n## Courses\n\n* [Causal Diagrams: Draw Your Assumptions Before Your Conclusions](https://www.edx.org/course/causal-diagrams-draw-your-assumptions-before-your-conclusions)\n* [Causal Inference: prediction, explanation, and intervention](http://www.skleinberg.org/teaching/CI18/index.html)\n* [Causal Inference Experiments Short Course](http://www.macartan.nyc/experiment/short-course/)\n* [ECON 305: Economics, Causality, and Analytics](http://www.nickchk.com/econ305.html) [\\[github\\]](https://github.com/NickCH-K/introcausality)\n* [Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells](https://www.complexityexplorer.org/courses/63-algorithmic-information-dynamics-a-computational-approach-to-causality-and-living-systems-from-networks-to-cells-2018/)\n* [Four Lectures on Causality by Jonas Peters](https://www.youtube.com/playlist?list=PLW01hpWnEtbTcuY0a0jhZyanHX3GPImAy)\n* [Julian Schuessler's Causal Graphs Seminar - Winner of 2019 American Statistics Association Causality in Statistics Education Award](http://www.julianschuessler.net/graphs2018.html)\n* [Ilya Shpitser's course on Causal Inference (Zip file) - Winner of 2017 American Statistics Association Causality in Statistics Education Award](https://www.amstat.org/asa/files/zipfiles/Causality-ShpitserMaterials.zip)\n* [Arvid Sjölander's course on Causal Inference (Zip file) - Winner of 2016 American Statistics Association Causality in Statistics Education Award](https://ww2.amstat.org/misc/causaliity/Sjolander-Supplemental.zip)\n* [Onyebuchi A. Arah course on Causality in Statistics (Dropbox folder) - Winner of 2016 American Statistics Association Causality in Statistics Education Award](https://www.dropbox.com/sh/mzuy3bewepwunye/AACn-zaBRAGMvxO-TVtCxH9Ba?dl=0)\n* [Introduction to causal inference by Maya L. Petersen \u0026 Laura B. Balzer](https://www.ucbbiostat.com/labs)\n* [Introduction to Causal Inference by Brady Neal](https://www.bradyneal.com/causal-inference-course)\n\n## Videos\n\n* [PyData LA 2018 Keynote: Judea Pearl - The New Science of Cause and Effect](https://www.youtube.com/watch?v=ZaPV1OSEpHw)\n* [CACM Mar. 2019 - The Seven Tools of Causal Inference](https://www.youtube.com/watch?v=CsMV5o3hotY)\n* [ACM Turing Award Lecture 2011 - Judea Pearl](https://amturing.acm.org/vp/pearl_2658896.cfm)\n* [Leon Bottou - Learning representations using causal invariance](https://www.facebook.com/iclr.cc/videos/534780673594799/)\n* [Online Causal Inference Seminar](https://www.youtube.com/channel/UCiiOj5GSES6uw21kfXnxj3A/videos)\n* [NeurIPS 2020 Workshop: Causal Discovery and Causality-Inspired Machine Learning](https://nips.cc/virtual/2020/protected/workshop_16110.html)\n* [Okke van der Wal - Personalization at Uber scale via causal-driven machine learning | PDAMS 2023](https://www.youtube.com/watch?v=c_dOpCvkNc0\u0026t=672s\u0026ab_channel=PyData)\n\n# Events\n\n## Workshops\n* [Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI](https://why19.causalai.net/#)\n* [Causality Challenge #1: Causation and Prediction](http://www.causality.inf.ethz.ch/challenge.php)\n* [NIPS 2013 Workshop on Causality](http://clopinet.com/isabelle/Projects/NIPS2013/)\n* [ChaLearn Fast Causation Coefficient Challenge](https://competitions.codalab.org/competitions/1381)\n\n\n# Communities, and Mailing lists\n\n* [Causality Challenge Google group](https://groups.google.com/forum/#!forum/causalitychallenge)\n\n\n# Miscellaneous\n\n* [Causal Inference Reading list](https://yanirseroussi.com/causal-inference-reading-list/)\n* [Causal inference paper reading list](https://web.archive.org/web/20190312230219/https://www.reddit.com/r/MachineLearning/comments/8lti7g/d_ml_beyond_curve_fitting_introduction_to_causal/dzipydw/)\n* [American Statistics Association Causality in Statistics Education Award](https://www.amstat.org/ASA/Your-Career/Awards/Causality-in-Statistics-Education-Award.aspx)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnapsternxg%2Fawesome-causality","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnapsternxg%2Fawesome-causality","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnapsternxg%2Fawesome-causality/lists"}