▲РЕПО НЕТ В ОКНЕ 180D. ПОКАЗАН FINDING ИЗ ОКНА 3D (3 days) — РАНГ #7188.
FINDING #7188 · UNIT ID 131646318
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.
SURPRISE SCORE
0.00
Score Breakdown
SCORE = SURPRISE × ENGAGEMENT^0.7 × FRESHNESS × VISIBILITY × CONFIDENCE
SURPRISE = WINDOW STARS / DAYS / (AUDIENCE + FLOOR)
0% OF STARS IN ARCHIVE
Growth Telemetry
VELOCITY /D
2.67
ACCEL
-1.00
RETENTION
50.0%
PEAK 2026-08-28 · FORK-RETENTION 0.0% · 8 STARS / WINDOW
Author Audience
AUDIENCE
5,237
FOLLOWERS
1,065
OWNER ★
15,537
Engagement Signals
FORKS
823
ISSUE AUTH
0
PR AUTH
0
UNIQUE STARGAZERS 8 / 8 (DIVERSITY 1.00)
Why This Is A Finding
py-why/EconML собрал 8 звёзд за окно, тогда как у автора всего 1,065 подписчиков — эффективная аудитория ≈ 5,237. Это даёт surprise-индекс 0.000505 (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 17% OF 8663 FINDINGS
METRICS IN CONTEXT
MEDIAN ACROSS ALL 8663 FINDINGS · Δ vs MEDIAN · PERCENTILE = SHARE RANKED BELOW
METRICVALUEMEDIANΔ MEDPERCENTILE
SCORE0.000.00-0.00ABOVE 17%
VELOCITY2.675.67-3.00ABOVE 0%
RETENTION50.0%41.7%+8.3 PPABOVE 55%
FORKS823332+491ABOVE 67%
SURPRISE0.000.00-0.00ABOVE 17%