{"adoption": {"forks": 358, "observed_at": "2026-08-28T04:03:17.714647+00:00", "stars": 1029}, "canonical_url": "https://ross.abutalabs.com/products/causal-inference-and-discovery-in-python", "card": {"archived": false, "artifact_type": "learning-resource", "description": "Causal Inference and Discovery in Python by Packt Publishing", "domain": ["machine-learning", "data-science", "artificial-intelligence", "tutorials"], "enriched": true, "function": ["machine-learning", "data-science", "nlp"], "health_score": 76, "homepage": null, "language": "Jupyter Notebook", "license": "MIT", "license_family": "permissive", "maturity": "active", "member_repos": ["PacktPublishing/Causal-Inference-and-Discovery-in-Python"], "name": "PacktPublishing/Causal-Inference-and-Discovery-in-Python", "platform": ["python", "jvm"], "pushed_at": "2026-07-15T07:16:08+00:00", "repo": "PacktPublishing/Causal-Inference-and-Discovery-in-Python", "stars": 1029, "tags": ["causal-inference", "causal-discovery", "book-code", "jupyter-notebooks", "dowhy", "econml", "pytorch", "pearl-causality", "counterfactuals", "structural-causal-models", "packt"], "topics": [], "urls": [], "use_cases": ["learn causal inference in python", "estimate causal effects with dowhy and econml", "discover causal graphs from data", "understand structural causal models and counterfactuals", "work through book exercises on causal machine learning", "apply causal discovery algorithms to a dataset"], "what_it_is": "The official code repository for the Packt book 'Causal Inference and Discovery in Python', containing Jupyter Notebook exercises and examples. It teaches Pearlian causal concepts, causal effect estimation, and causal discovery using libraries like DoWhy, EconML, and PyTorch.", "when_to_avoid": ["you need a production-ready causal inference library rather than educational code", "you want a maintained software package with an API instead of book companion notebooks", "you need causal inference tooling in a language other than Python"], "when_to_choose": ["you are reading the book and want runnable code for each chapter", "you want hands-on Python notebooks covering causal inference from basics to advanced methods", "you want practical examples using DoWhy, EconML, and PyTorch for causal analysis"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/causal-inference-and-discovery-in-python", "repo": "PacktPublishing/Causal-Inference-and-Discovery-in-Python", "role": "main", "score": 74}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:17.714647+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T07:07:37.540941+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "ebe179d1909b7d37c5b9b514443d9e34420f82482d36861f79e4e2e836519e7e", "fetched_at": "2026-08-28T04:03:17.714647+00:00", "kind": "readme", "missing": false, "url": "https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 92, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 1591, "days_push": 49, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 74, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}