القائمة
Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A/B testing, or policy evaluation.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when operating SkillX full-loop experiments after an inner-loop run completes, especially to audit result validity, classify failures, decide required/recommended reruns, prepare targeted rerun manifests, or determine whether it is safe to proceed to outer-loop optimization.
An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.
An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.
An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.
This is a summary the advantages and disadvantages of earthquake event detection and phase picking methods, shared by leading seismology researchers at the 2025 Earthquake Catalog Workshop. Use it when you have a seismic phase picking task at hand.
Clean messy tabular datasets with deduplication, missing value imputation, outlier handling, and text processing. Use when dealing with dirty data that has duplicates, nulls, or inconsistent formatting.
| name | did_causal_analysis |
| description | Difference-in-Differences causal analysis to identify demographic drivers of behavioral changes with p-value significance testing. Use for event effects, A/B testing, or policy evaluation. |
DiD framework for causal inference with automatic method selection (Multivariate vs Univariate) based on sample size.
Both methods use: Y = α + βX + γPost + δ(X × Post) + ε where δ is the DiD estimate.
Multivariate Heterogeneous DiD
n ≥ k × min_sample_ratio (default: 10k samples minimum)Univariate DiD
CRITICAL: Both use statsmodels regression to provide p-values for statistical inference.
CORRECT approach - Only aggregate actual transactions:
# Group actual transactions by entity-period
intensive_df = transactions.groupby(['entity_id', 'period'])['value'].sum().reset_index()
# Result: Only rows where events occurred (e.g., 3,404 rows for active participants)
Why this matters: Intensive margin measures "how much MORE did participants engage", not "did non-participants start engaging". Including zeros for non-participants dilutes the effect and changes the research question.
CORRECT approach - Create complete entity × group × period grid:
# Create all possible combinations
from itertools import product
all_entities = entities_df['entity_id'].unique()
all_groups = target_groups # e.g., categories, regions, products
all_periods = ['baseline', 'treatment']
complete_grid = pd.DataFrame(
list(product(all_entities, all_groups, all_periods)),
columns=['entity_id', 'period']
)
# Mark who participated (1) vs who didn't (0)
events_lookup = transactions[['entity_id', 'period']].drop_duplicates()
events_lookup['participated'] = 1
extensive_df = complete_grid.merge(events_lookup, how='left')
extensive_df['participated'] = extensive_df['participated'].fillna(0).astype(int)
# Result: All entities × groups × periods with binary outcome
Why this matters: Extensive margin measures "did the participation rate increase", which requires knowing both who participated AND who didn't participate.
DIDAnalyzer(min_sample_ratio=10, significance_level=0.05, min_group_size=1)min_sample_ratio=10: Samples-to-features ratio for multivariate DiDsignificance_level=0.05: P-value threshold 0.05-0.1 based on the context and requirementmin_group_size=1: Min observations per group for univariate DiDintensive_margin(df, features, value_col, top_n=None, sort_by='estimate', asc=False)Analyzes intensive margin: how much behavior changed among participants.
Input Data: Sparse entity-period data (only participants)
df: Sparse DataFrame - only entities with participationfeatures: List of demographic column namesvalue_col: Continuous outcome column (e.g., 'amount', 'spending')top_n: Top N features (None = all)sort_by: 'estimate' or 'p_value'asc: True for ascending, False for descendingReturns: List of {"feature": str, "did_estimate": float, "p_value": float, "method": str}
extensive_margin(df, features, value_col='purchased', top_n=None, sort_by='estimate', asc=False)Analyzes extensive margin: how many people changed behavior.
Input Data: Complete entity-period panel (all entities including non-participants)
df: Complete DataFrame - ALL entities × ALL periods (includes non-participants)features: List of demographic column namesvalue_col: Binary outcome column (0/1), default 'purchased'top_n: Top N features (None = all)sort_by: 'estimate' or 'p_value'asc: True for ascending, False for descendingReturns: List of {"feature": str, "did_estimate": float, "p_value": float, "method": str}