Individual skill
evo-trend-anomaly-causal-inference
Complete pipeline for identifying anomalous product category spending patterns in e-commerce data using Prophet counterfactual forecasting, and explaining anomalies through demographic factors via Difference-in-Differences causal inference.
tasks-evolved/trend-anomaly-causal-inference/environment/skills/evo-trend-anomaly-causal-inference/SKILL.md
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SKILL.mdcore · 3,311 bytesSKILL.mdcore · 3,311 bytesscripts/anomaly_detection.pyruns · 5,342 bytesscripts/clean_data.pyruns · 6,396 bytesscripts/feature_engineering.pyruns · 3,961 bytes
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# Evo Trend Anomaly Causal Inference
Complete pipeline for identifying anomalous product category spending patterns and using DiD causal inference to explain anomalies through demographic factors.
## Pipeline Steps
1. **Data Cleaning** (`scripts/clean_data.py`): Clean survey and purchase data, handling duplicates, missing values, inconsistent formatting, date parsing, monetary columns, and category extraction.
2. **Anomaly Detection** (`scripts/anomaly_detection.py`): Prophet-based counterfactual forecasting to compute deviation index (-100 to 100) per category for March 2020.
3. **Feature Engineering** (`scripts/feature_engineering.py`): One-hot/ordinal encode demographics, bin continuous variables, produce feature-engineered survey CSV.
4. **DiD Analysis** (`scripts/did_analysis.py`): Difference-in-Differences causal analysis for intensive margin (univariate DiD on spend among purchasers) and extensive margin (multivariate heterogeneous DiD on purchase propensity).
## Usage
```python