PyRank
d2cml-ai

synthdid

Python package for causal inference via Synthetic Difference-in-Differences. Supports block and staggered adoption designs, optional covariates, unit/time weight visualization, and standard error estimation.
PyPI GitHub
More links
Download Stats
Monthly downloads
598
Weekly downloads
206
GitHub stars
25

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

Package Information

Latest Version
0.10.1
Requires Python
Not specified
Licence
MIT
Last Release
2 years ago
Author
D2CML Team, Alexander Quispe, Rodrigo Grijalba, Jhon Flores, Franco Caceres
First Release
April 17, 2023
Total Releases
12
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
40
Popularity
29
Adoption
27
Community
50
Provenance
0
How the maintenance status was determined
Last commit
206 days ago
Monthly downloads
598
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
598
Stars
25
Stars per 1k downloads
41.81
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
Needs at least 1,000 monthly downloads to classify popularity.
How download momentum is calculated
Week daily avg
29
Month daily avg
20
Momentum
—
((week_daily_avg - month_daily_avg) / month_daily_avg) * 100
Momentum not reported for packages with < 5,000 monthly downloads — percentages are noisy at low volume.
More insights and metrics
Python version support tiers
requires_python
Not specified
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
Package does not declare a minimum Python version.
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
841 days ago
Total releases
12
Releases/year
3.4
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
7
Monthly downloads
598
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
46
 
Issue Burden
-
per 1k stars
Discussions
-
per 1k stars
Fork Ratio
77.8%
contrib / forks
Stale Release
635d
 
PR Velocity
Quiet
 
Not all downloads include detailed metadata. Breakdowns reflect the subset that does.

Downloads by OS

Linux 9
Windows 2
macOS 2

Python Versions

3.11
38.5% 5
3.10
23.1% 3
3.12
15.4% 2
3.9
15.4% 2
3.13
7.7% 1

Top Countries

1. US US
11
2. CA CA
1
3. HK HK
1

Weekly Downloads

GitHub Stars

Dependencies

25

Used By

0
Not used by any tracked packages

Repository details

Forks
9
Issues
7
Last Commit
6 months ago
Contributors
7
Open PRs
1
Archived
No

Data and sources

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