py-why / EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
py-why/EconML holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (47/100). It was last updated 2 days ago. A single contributor accounts for most of its recent work.
Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.
Score profile
Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.
The weighted overall 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).
Ownership
This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.
Package ecosystems
| Registry | Package | Version | Downloads / mo | Versions | Last publish | Tags |
|---|---|---|---|---|---|---|
| PyPI | econml | 0.17.0 | 348,680 | 35 | 79 days ago | treatment-effect |
Metrics by category
Vitality
Is the project alive — is code being written and are releases shipping?
Development activity
How it's scored
| 36/36 | Push recency — last push 2 days ago |
| 9.7/36 | Commit cadence — 14/52 weeks with commits |
| 14/18 | Commit volume — 35 commits in the last year |
| 10/10 | OpenSSF Scorecard: Maintained — 12 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10 |
Inputs used
| commits_last_year | 35 |
| human_commit_share | 0.93 |
| days_since_last_push | 2 |
| active_weeks_last_year | 14 |
Release discipline
How it's scored
| 27/27 | Ships releases — 35 releases published |
| 36/36 | Release recency — latest release 75 days ago |
| 12.6/27 | Release cadence — a release every ~201.8 days |
| 0/10 | OpenSSF Scorecard: Signed-Releases — no data |
Inputs used
| releases_count | 35 |
| latest_release_tag | v0.17.0 |
| releases_from_tags | no |
| days_since_latest_release | 75 |
| mean_days_between_releases | 201.8 |
Community & Adoption
Does the project have users, downloads, attention, and a welcoming setup for contributors?
Popularity & adoption
How it's scored
| 59.7/60 | Stars — 4,817 stars |
| 24.3/25 | Forks — 832 forks |
| 10.8/15 | Watchers — 90 watchers |
Inputs used
| forks | 832 |
| stars | 4,817 |
| watchers | 90 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community health
How it's scored
| 22.5/22.5 | README |
| 16.9/22.5 | License — license file present, not a recognized license |
| 0/18 | CONTRIBUTING guide |
| 0/13.5 | Code of conduct |
| 0/7.2 | Issue template |
| 0/6.3 | PR template |
Inputs used
| has_readme | yes |
| has_license | yes |
| readme_badges | 4 |
| has_contributing | no |
| has_issue_template | no |
| has_code_of_conduct | no |
| readme_badge_services | github.com, shields.io |
| has_pull_request_template | no |
Ecosystem adoption (downloads)
How it's scored
| 73.9/80 | Monthly downloads — 348,680 downloads/month across pypi |
| 0/20 | Registry dependents — not reported by this ecosystem |
Inputs used
| packages | econml |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 348,680 |
| unverified_packages_excluded | — |
Sustainability & Governance
Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?
How it's scored
| 9/54 | Bus factor — 1 contributor(s) cover half of all commits |
| 7.2/22.5 | Commit distribution — top contributor authored 68% of commits |
| 13.5/13.5 | Contributor breadth — 49 contributors |
| 10/10 | OpenSSF Scorecard: Contributors — project has 9 contributing companies or organizations |
Inputs used
| bus_factor | 1 |
| contributors_sampled | 49 |
| top_contributor_share | 0.679 |
How it's scored
| 16.4/42 | Issue resolution — 39% of issues closed |
| 25.2/30 | PR acceptance — 332/395 decided PRs merged |
| 0/13 | Newcomer PR acceptance — no first-time contributor's PR decided in 30d |
| 15/15 | OpenSSF Scorecard: Code-Review — all changesets reviewed |
Inputs used
| merged_prs | 332 |
| open_issues | 383 |
| closed_issues | 245 |
| prs_merged_7d | 0 |
| prs_decided_7d | 0 |
| prs_merged_30d | 1 |
| prs_decided_30d | 1 |
| issue_closed_ratio | 0.39 |
| closed_unmerged_prs | 63 |
| first_time_authors_30d | 0 |
| first_time_prs_merged_30d | 0 |
| first_time_prs_decided_30d | 0 |
Ownership & stewardship
How it's scored
| 30/30 | Ownership backing — organization-owned |
| 0/20 | Verified domain |
| 21.8/25 | Owner reach — 1,073 followers of py-why |
| 17.8/25 | Track record — 14 public repos, account ~4 yr old |
Inputs used
| followers | 1,073 |
| owner_type | Organization |
| is_verified | no |
| owner_login | py-why |
| public_repos | 14 |
| account_age_days | 1,684 |
Package maintenance
How it's scored
| 25/25 | Published & resolvable — 1 package(s) on pypi |
| 35/35 | Publish recency — latest publish 79 days ago |
| 20/20 | Version history — 35 published versions |
| 20/20 | Not deprecated — active, not deprecated or yanked |
Inputs used
| packages | econml |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 79 |
Engineering Quality
Are baseline engineering and documentation practices in place?
Engineering practices
How it's scored
| 24/24 | CI workflows — 3 workflow(s) |
| 24/24 | Tests present |
| 16/16 | Linter config — pyproject.toml ([tool.ruff]) |
| 9.6/9.6 | Pre-commit hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 16 out of 16 merged PRs checked by a CI test -- score normalized to 10 |
Inputs used
| has_ci | yes |
| has_tests | yes |
| has_editorconfig | no |
| has_linter_config | yes |
| has_precommit_config | yes |
Documentation
How it's scored
| 30/30 | README |
| 25/25 | Documentation directory |
| 15/15 | Documentation / homepage site — https://www.microsoft.com/en-us/research/project/alice/ |
| 10/10 | Repository description |
| 10/10 | Topics — 6 topics |
| 10/10 | Wiki |
Inputs used
| topics | machine-learning, economics, causal-inference, causality, econometrics, treatment-effects |
| has_wiki | yes |
| homepage | https://www.microsoft.com/en-us/research/project/alice/ |
| docs_site | https://www.microsoft.com/en-us/research/project/alice/ |
| has_readme | yes |
| has_docs_dir | yes |
| has_description | yes |
Security
Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
Security posture
How it's scored
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 6/7.5 | Branch-Protection — branch protection is not maximal on development and all release branches |
| 2.5/2.5 | CI-Tests — 16 out of 16 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 7.5/7.5 | Code-Review — all changesets reviewed |
| 2.5/2.5 | Contributors — project has 9 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.2/2.5 | License — license file detected |
| 7.5/7.5 | Maintained — 12 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 5/5 | Security-Policy — security policy file detected |
| 0/7.5 | Signed-Releases — no data |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Inputs used
| source | openssf_scorecard |
| checks_evaluated | 17 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 1 |
| scorecard_aggregate | 6.5 |
Dependency advisories
How it's scored
| 35/35 | Direct dependencies free of known advisories — no direct dependency carries a known advisory |
| 25/25 | Indirect dependencies free of known advisories — no indirect dependency carries a known advisory |
| 0/40 | No advisories left outstanding — no advisory carries a publication date |
Inputs used
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 24 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
AI Readiness
How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.
Agent context & guidance
How it's scored
| 0/45 | Agent instructions — no CLAUDE.md / AGENTS.md / editor rules |
| 0/15 | Machine-readable docs (llms.txt) |
| 25.2/40 | Legible commit history — 44 of 93 human commits state their intent (structured subject or explanatory body) |
Inputs used
| has_llms_txt | no |
| llms_txt_url | — |
| legible_history_share | 0.473 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
How it's scored
| 0/18 | One-command bootstrap |
| 22/22 | Automated tests |
| 11/11 | Lint / format config — pyproject.toml ([tool.ruff]) |
| 0/11 | Static type checking |
| 0/10 | Reproducible environment |
| 10/10 | Demonstrated agent practice — 16 of the last 100 commits agent-authored or agent-credited |
| 0/8 | Automated maintenance — no automated dependency updates observed |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Inputs used
| has_nix | no |
| has_tests | yes |
| lockfiles | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | — |
| has_devcontainer | no |
| has_linter_config | yes |
| typecheck_configs | — |
| agent_commit_share | 0.16 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Code legibility for models
How it's scored
| 0/45 | Type-checkable code — Jupyter Notebook without a type-check config |
| 50.3/55 | Manageable file sizes — 12/139 source files over 60KB |
Inputs used
| primary_language | Jupyter Notebook |
| largest_source_bytes | 144,560 |
| source_files_sampled | 139 |
| oversized_source_files | 12 |
Machine-readable interfaces
How it's scored
| 0/40 | API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software |
| 0/20 | MCP server — not applicable to this kind of software |
| 40/40 | Runnable examples — notebooks |
Inputs used
| example_dirs | notebooks |
| has_mcp_signal | no |
| api_schema_files | — |
| interfaces_expected_of | — |
Key facts
Data collection warnings
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
More detail
Star and fork history 0 ★ / 832 ⇿
When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.
Each point covers 8 days.
OpenSSF Scorecard 6.5 / 10
Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 8 | Branch-Protection | branch protection is not maximal on development and all release branches |
| 10 | CI-Tests | 16 out of 16 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 10 | Code-Review | all changesets reviewed |
| 10 | Contributors | project has 9 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 9 | License | license file detected |
| 10 | Maintained | 12 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 10 | Security-Policy | security policy file detected |
| n/a | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Direct dependencies 11
| Registry | Package | Version constraint | Manifest |
|---|---|---|---|
| PyPI | numpy | >= 2.0.2 | pyproject.toml |
| PyPI | numba | >= 0.60.0 | pyproject.toml |
| PyPI | scipy | >= 1.13.1 | pyproject.toml |
| PyPI | scikit-learn | >= 1.6.0, < 1.10 | pyproject.toml |
| PyPI | sparse | >= 0.15.4 | pyproject.toml |
| PyPI | joblib | >= 1.4.2 | pyproject.toml |
| PyPI | statsmodels | >= 0.14.4 | pyproject.toml |
| PyPI | pandas | >= 2.2.3 | pyproject.toml |
| PyPI | shap | >= 0.46.0 | pyproject.toml |
| PyPI | lightgbm | >= 4.5.0 | pyproject.toml |
| PyPI | packaging | >= 24.2 | pyproject.toml |
All dependencies 18
Full resolved dependency set from the GitHub dependency graph: 11 direct and 7 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.
| Registry | Package | Version | Relation |
|---|---|---|---|
| PyPI | joblib | — | direct |
| PyPI | lightgbm | — | direct |
| PyPI | numba | — | direct |
| PyPI | numpy | — | direct |
| PyPI | packaging | — | direct |
| PyPI | pandas | — | direct |
| PyPI | scikit-learn | — | direct |
| PyPI | scipy | — | direct |
| PyPI | shap | — | direct |
| PyPI | sparse | — | direct |
| PyPI | statsmodels | — | direct |
| PyPI | cython | — | indirect |
| PyPI | dowhy | — | indirect |
| PyPI | graphviz | — | indirect |
| PyPI | matplotlib | — | indirect |
| PyPI | ray | — | indirect |
| PyPI | setuptools | — | indirect |
| PyPI | wheel | — | indirect |
Dependency advisories 0
Installing pypi:econml@0.17.0 pulls in 24 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.
No known advisories affect the assessed dependencies.
An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.
Raw JSON report machine-readable
Feedback
Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.
Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.
Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.
How one result sits in the wider record: aggregate statistics — PyPI.