{"id":25214009,"url":"https://github.com/jra333/causal-lift-analysis","last_synced_at":"2026-04-28T20:01:58.526Z","repository":{"id":276235729,"uuid":"928191643","full_name":"jra333/Causal-Lift-Analysis","owner":"jra333","description":"A causal lift analysis demonstrating the impact of certain metrics have on sales + POC app usecase.","archived":false,"fork":false,"pushed_at":"2025-02-07T02:56:51.000Z","size":723,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T08:28:03.293Z","etag":null,"topics":["analytics","causal-inference","predictive-modeling","regression","streamlit","structual-equation-modelling"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jra333.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2025-02-06T08:10:35.000Z","updated_at":"2025-02-07T03:04:27.000Z","dependencies_parsed_at":"2025-02-07T03:37:12.416Z","dependency_job_id":null,"html_url":"https://github.com/jra333/Causal-Lift-Analysis","commit_stats":null,"previous_names":["jra333/sem_causal_lift"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jra333/Causal-Lift-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jra333%2FCausal-Lift-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jra333%2FCausal-Lift-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jra333%2FCausal-Lift-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jra333%2FCausal-Lift-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jra333","download_url":"https://codeload.github.com/jra333/Causal-Lift-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jra333%2FCausal-Lift-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32396781,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-28T19:38:08.556Z","status":"ssl_error","status_checked_at":"2026-04-28T19:37:55.688Z","response_time":56,"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":["analytics","causal-inference","predictive-modeling","regression","streamlit","structual-equation-modelling"],"created_at":"2025-02-10T16:58:13.334Z","updated_at":"2026-04-28T20:01:58.503Z","avatar_url":"https://github.com/jra333.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Structural Equation Modeling (SEM) Analysis \u0026 App\n\n**Two folders include:**\n- SEM [App](https://github.com/jra333/sem_causal_lift/tree/main/sem_app_testing) POC using Streamlit\n\nAn interactive web application built with [Streamlit](https://streamlit.io/) that enables users to perform Structural Equation Modeling (SEM) on their data. The app integrates data upload, preprocessing (with time series support), dynamic model specification, SEM analysis using [semopy](https://github.com/semopy/semopy), and AI-generated interpretations via Google Gemini.\n\n- Causal Impact Analysis [Notebook](https://github.com/jra333/sem_causal_lift/tree/main/sem_notebooks)\n\nThe integrated impact analysis was able to prove that upper-funnel metrics contribute to sales through modeling and predicting sales using a scenario based approach. Explored and confirmed the lift in sales using a custom structural equation model highlighting the pathways in upper-funnel that lead to sales increase.\n\n  - Performs comprehensive data aggregation (daily, weekly, and monthly) on raw sales data.\n  - Implements advanced visualization techniques including time series plots, correlation heatmaps, and channel comparison plots.\n  - Conducts integrated causal impact analysis combining Random Forest and OLS regression approaches.\n  - Provides detailed elasticity analysis with statistical inferences and seasonal impact assessments.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjra333%2Fcausal-lift-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjra333%2Fcausal-lift-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjra333%2Fcausal-lift-analysis/lists"}