py-why/dowhy — GitHub trend

FINDING #7393 · UNIT ID 135584946

py-why/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

[ PYTHON ][ ORG ][ GITHUB ↗ ]

SURPRISE SCORE

0.00

Score Breakdown

SURPRISE0.000636

ENGAGEMENT0.20

FRESHNESS1.00

SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE

SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)

0% OF STARS IN ARCHIVE

Growth Telemetry

VELOCITY /D

3.33

ACCEL

-0.50

RETENTION

50.0%

PEAK 2026-08-09 · FORK-RETENTION 0.0% · 10 STARS / WINDOW

Author Audience

AUDIENCE

5,204

FOLLOWERS

1,055

OWNER ★

15,470

Engagement Signals

FORKS

1,047

ISSUE AUTH

PR AUTH

UNIQUE STARGAZERS 10 / 10 (DIVERSITY 1.00)

Why This Is A Finding

py-why/dowhy собрал 10 звёзд за окно, тогда как у автора всего 1,055 подписчиков — эффективная аудитория ≈ 5,204. Это даёт surprise-индекс 0.000636 (звёзды относительно охвата автора, а не в абсолюте). Удержание форков 0.0% и 0 внешних контрибьюторов отделяют реальный инструмент от разовой вспышки. Акселерация отрицательная — внимание остывает после пика.

Related Findings

RANKS ABOVE 19% OF 9133 FINDINGS

#04semantica-agi/semantica[ PYTHON ]0.94 #06vitali87/code-graph-rag[ PYTHON ]0.85 #08Pan-Chera/Multi-Agent-CAD[ PYTHON ]0.63 #12lbx154/Argus[ PYTHON ]0.51 #22531149627/MonsterDeleter[ PYTHON ]0.39

METRICS IN CONTEXT

MEDIAN ACROSS ALL 9133 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW

METRICVALUEMEDIANΔ MEDPERCENTILE

SCORE0.000.00-0.00ABOVE 19%

VELOCITY3.335.67-2.33ABOVE 18%

RETENTION50.0%37.5%+12.5 PPABOVE 57%

FORKS1,047327+720ABOVE 72%

SURPRISE0.000.00-0.00ABOVE 18%