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.
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Download Stats Bug Tracker
Verified repo
Monthly downloads
368.6K
Weekly downloads
92.3K
GitHub stars
4.8K

Downloads in the 30 and 7 days to October 10, 2026. Refreshed weekly.

Package Information

Latest Version
0.17.0
Requires Python
>=3.9
Licence
MIT
Last Release
2 months ago
Author
PyWhy contributors
First Release
March 6, 2019
Total Releases
35
Python Versions
3.9 3.10 3.11 3.12 3.13 3.14
No reported malware or high/critical advisories · Details

The current version has no OSV.dev malware reports and no critical or high-severity vulnerabilities.

Package Insights

What's this?
Score breakdown - each component scores 0-100, weighted by percentage
Maintenance
90
Popularity
80
Adoption
75
Community
70
Provenance
100
How the maintenance status was determined
Last commit
14 days ago
Monthly downloads
368,615
• 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
368,615
Stars
4,810
Stars per 1k downloads
13.05
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
13,180
Month daily avg
12,287
Momentum
+7.3%
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
More insights and metrics
Python version support tiers
requires_python
>=3.9
Cutting Edge
Minimum supported version is 3.14 or newer
Modern
Minimum supported version is 3.11 to 3.13
• Legacy
Supports 3.10 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
76 days ago
Total releases
35
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
368,615
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
67
 
Issue Burden
79.6
per 1k stars
Discussions
0.0
per 1k stars
Fork Ratio
6.8%
contrib / forks
Stale Release
61d
 
PR Velocity
Active
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 5.9K
Windows 115
macOS 54

Python Versions

3.11
28.2% 1.5K
3.12
21.2% 1.2K
3.10
17.6% 955
3.9
16.5% 900
3.13
16.5% 895

Top Countries

1. US US
4.6K
2. IE IE
714
3. SG SG
162
4. AE AE
121
5. DE DE
121
6. IN IN
109
7. SE SE
96
8. KR KR
94
9. NL NL
92
10. GB GB
83

Weekly Downloads

GitHub Stars

Dependencies

11
10 optional dependencies
  • azure-cli[all]
    extra == "all"
  • azure-cli[automl]
    extra == "automl"
  • dowhy<0.15,>=0.13[all]
    extra == "all"
  • dowhy<0.15,>=0.13[dowhy]
    extra == "dowhy"
  • graphviz>=0.20.3[all]
    extra == "all"
  • graphviz>=0.20.3[plt]
    extra == "plt"
  • matplotlib>=3.9.4[plt]
    extra == "plt"
  • matplotlib>=3.9.4[all]
    extra == "all"
  • ray>=2.40.0[ray]
    extra == "ray"
  • ray>=2.40.0[all]
    extra == "all"

Used By

19
View all 19 dependents

Repository details

Forks
828
Issues
383
Last Commit
2 weeks ago
Contributors
56
Open PRs
43
Archived
No

Data and sources

Tracking Since
September 26, 2026
Data Updated
October 11, 2026
Data Freshness
Fresh
Keywords
treatment-effect
Sources and methodology