PyRank
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.
PyPI GitHub Verified repo
No known security advisories. The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

econml has 593K monthly downloads and 5K GitHub stars. Version 0.16.0 supports Python 3.9+, first published in 2019.

It is actively maintained under a permissive license, last released in Jul 2025.

It is built on top of joblib, lightgbm and numpy. It is used by 17 tracked packages, including responsibleai, coreason-runtime and clearner.

GitHub topics include machine-learning, economics and causal-inference.

Downloads
Day
14.3K
Week
132.7K
Month
593.1K
Stars
4.7K
Forks
815
Issues
382
Version
0.16.0

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
90
Popularity
80
Adoption
67
Community
70
Provenance
100
How the maintenance status was determined
Last commit
25 days ago
Monthly downloads
593,077
• Active
Activity within 30 days
Stable
Activity within 31-180 days
Minimal
Activity within 181-365 days, or no activity data
At Risk
No activity for > 365 days, but > 10,000 monthly downloads
Archived
Repository flagged as archived on GitHub
How the popularity category was determined
Monthly downloads
593,077
Stars
4,701
Stars per 1k downloads
7.93
Industry Standard
> 10,000,000 monthly downloads
Hidden Gem
< 0.5 stars per 1k downloads and > 100,000 monthly downloads
• Community Favorite
> 5 stars per 1k downloads
Rising Star
Balanced ratio of stars to downloads
Unknown
Fewer than 1,000 monthly downloads, or no star data
How download momentum is calculated
Week daily avg
18,961
Month daily avg
19,769
Momentum
-4.1%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Python version support tiers
requires_python
>=3.9
Cutting Edge
Minimum supported version is 3.12 or newer
• Modern
Minimum supported version is 3.9, 3.10, or 3.11
Legacy
Supports 3.8 or older (or Python 2)
Unknown
No requires_python specified or unparseable
License categories
License
MIT
• Permissive
MIT, Apache, BSD, ISC, Unlicense, CC0, etc.
Copyleft
GPL, LGPL, AGPL, MPL, EPL, etc.
Proprietary
Commercial, all rights reserved, custom EULAs
Unknown
No license specified, or none of the above match
How the release cadence was determined
Last release
361 days ago
Total releases
34
Releases/year
4.6
Rapid
Last release within 365 days and > 12 releases/year
• Regular
Last release within 365 days
Slow
Last release 366-730 days ago
Stalled
Last release > 730 days ago, or no releases
Unknown
No usable release date available
How contributor risk was determined
Contributors
56
Monthly downloads
593,077
High Load
≤ 2 contributors and ≥ 50,000 monthly downloads
Moderate Load
< 10 contributors and ≥ 500,000 monthly downloads
Low Load
< 25 contributors and ≥ 5,000,000 monthly downloads, or sole maintainer with ≥ 10,000 monthly downloads
• Balanced
Enough contributors for the package's popularity
Unknown
No contributor data available
Engagement
68
 
Issue Burden
81.3
per 1k stars
Discussions
0.0
per 1k stars
Fork Ratio
6.9%
contrib / forks
Stale Release
336d
 
PR Velocity
Active
 

Weekly Downloads

GitHub Stars

Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 13.2K
Windows 120
macOS 90

Python Versions

3.10
43.6% 5.6K
3.11
22.9% 3.0K
3.12
20.7% 2.7K
3.9
8.4% 1.1K
3.13
4.4% 564

Top Countries

1. US US
6.9K
2. IE IE
4.8K
3. NL NL
260
4. KR KR
230
5. SE SE
216
6. AE AE
165
7. JP JP
149
8. IN IN
127
9. FR FR
111
10. SG SG
104
GitHub Topics
Tags from the package's GitHub repository describing what it does.

Package Information

License
MIT
First Release
March 6, 2019
Last Release
12 months ago
Last Commit
3 weeks ago
Latest Version
0.16.0
Total Releases
34
Contributors
56
Open PRs
37
Archived
No
Author
PyWhy contributors
Python Versions
3.9 3.10 3.11 3.12 3.13
Wheels
cp310 cp311 cp312 cp313 cp36 cp37 cp38 cp39 py3
Keywords
treatment-effect
Tracking Since
January 6, 2026
Data Updated
July 5, 2026
Data Freshness
Fresh

Links