{"id":13401464,"url":"https://github.com/google/CausalImpact","last_synced_at":"2025-03-14T07:31:40.386Z","repository":{"id":19518438,"uuid":"22765467","full_name":"google/CausalImpact","owner":"google","description":"An R package for causal inference in time series","archived":false,"fork":false,"pushed_at":"2023-07-17T18:19:58.000Z","size":3308,"stargazers_count":1720,"open_issues_count":34,"forks_count":255,"subscribers_count":104,"default_branch":"master","last_synced_at":"2025-01-07T16:07:29.434Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/google.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}},"created_at":"2014-08-08T17:16:13.000Z","updated_at":"2024-12-27T16:47:46.000Z","dependencies_parsed_at":"2023-01-13T20:25:27.399Z","dependency_job_id":"294f4ccd-1aa7-4733-8b3b-805e8d998677","html_url":"https://github.com/google/CausalImpact","commit_stats":{"total_commits":79,"total_committers":14,"mean_commits":5.642857142857143,"dds":0.6329113924050633,"last_synced_commit":"e38049fdf1d2a1aab76ca5eaf47651af4d581795"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2FCausalImpact","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2FCausalImpact/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2FCausalImpact/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google%2FCausalImpact/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/google","download_url":"https://codeload.github.com/google/CausalImpact/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243482901,"owners_count":20297902,"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":"2024-07-30T19:01:02.951Z","updated_at":"2025-03-14T07:31:40.380Z","avatar_url":"https://github.com/google.png","language":"R","funding_links":[],"categories":["R","Causal Inference","其他_机器视觉","Open-Source MMM \u0026 Incrementality","Machine Learning","Tools","Tools and Algorithms"],"sub_categories":["网络服务_其他","Causal Inference"],"readme":"# CausalImpact\n\n\u003c!-- badges: start --\u003e\n[![R-CMD-check](https://github.com/google/CausalImpact/workflows/R-CMD-check/badge.svg)](https://github.com/google/CausalImpact/actions)\n[![Codecov test coverage](https://codecov.io/gh/google/CausalImpact/branch/master/graph/badge.svg)](https://app.codecov.io/gh/google/CausalImpact?branch=master)\n\u003c!-- badges: end --\u003e\n\n## An R package for causal inference using Bayesian structural time-series models\n\nThis R package implements an approach to estimating the causal effect of a\ndesigned intervention on a time series. For example, how many additional daily\nclicks were generated by an advertising campaign? Answering a question like this\ncan be difficult when a randomized experiment is not available. The package aims\nto address this difficulty using a structural Bayesian time-series model to\nestimate how the response metric might have evolved after the intervention if\nthe intervention had not occurred.\n\nAs with all approaches to causal inference on non-experimental data, valid\nconclusions require strong assumptions. The CausalImpact package, in particular,\nassumes that the outcome time series can be explained in terms of a set of\ncontrol time series that were themselves not affected by the intervention.\nFurthermore, the relation between treated series and control series is assumed\nto be stable during the post-intervention period. Understanding and checking\nthese assumptions for any given application is critical for obtaining valid\nconclusions.\n\n## Installation\n\n```r\ninstall.packages(\"CausalImpact\")\nlibrary(CausalImpact)\n```\n\n## Getting started\n\n[Video tutorial](https://www.youtube.com/watch?v=GTgZfCltMm8)\n\n[Documentation and examples](https://google.github.io/CausalImpact/CausalImpact.html)\n\n## Further resources\n\n*   Manuscript:\n    [Brodersen et al., Annals of Applied Statistics (2015)](https://research.google/pubs/pub41854/)\n\n*   For questions on the statistics behind CausalImpact:\n    [Cross Validated](https://stats.stackexchange.com/questions/tagged/causalimpact)\n\n*   For questions on how to use the CausalImpact R package:\n    [Stack Overflow](https://stackoverflow.com/questions/tagged/causalimpact)\n\n*   [Bug reports](https://github.com/google/CausalImpact/issues)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2FCausalImpact","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle%2FCausalImpact","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle%2FCausalImpact/lists"}