# Causal Discovery Algorithm Selection Meta-Learner

A meta-learning system that predicts the **top-3 best causal discovery algorithms** for any discrete observational dataset, based on dataset meta-features.

## 🎯 What it Does

Given a new discrete dataset (pandas DataFrame), the system:
1. **Extracts 34 meta-features** (entropy, mutual information, chi² statistics, CI test probes, etc.)
2. **Predicts normalized SHD** for each of 9 algorithms via a trained Random Forest
3. **Ranks and returns the top-3** algorithms expected to produce the most accurate CPDAG

## 📊 Performance (Leave-One-Network-Out Cross-Validation)

| Metric | Value |
|--------|-------|
| **Top-3 Hit Rate** | **67.2%** (true best algorithm is in predicted top-3) |
| **NDCG@3** | **0.947** (ranking quality) |
| **Mean Regret** | **0.012** (tiny SHD gap vs oracle selection) |
| **Median Regret** | **0.000** (majority of predictions are perfect) |

Evaluated on 116 benchmark configs across 13 bnlearn networks (5–70 nodes).

## 🧪 Algorithm Pool (9 algorithms)

| Algorithm | Family | Library | Output |
|-----------|--------|---------|--------|
| **PC** | Constraint-based | causal-learn | CPDAG |
| **FCI** | Constraint-based | causal-learn | PAG |
| **GES** | Score-based | causal-learn | CPDAG |
| **BOSS** | Permutation-based | causal-learn | CPDAG |
| **GRaSP** | Permutation-based | causal-learn | CPDAG |
| **HC** | Score-based (greedy) | pgmpy | DAG |
| **Tabu** | Score-based (meta-heuristic) | pgmpy | DAG |
| **MMHC** | Hybrid | pgmpy | DAG |
| **K2** | Score-based (ordering) | pgmpy | DAG |

## 🔬 Key Insight: Dependency Parsing Connection

This project was inspired by a structural parallel between **NLP dependency parsing** and **causal discovery**:
- Both predict **directed graphs** over nodes (words/variables)
- Both have **ground-truth annotations** (treebanks/bnlearn networks)
- Both use **arc-level evaluation** (UAS/LAS ↔ SHD/F1)

The biaffine pairwise scoring mechanism from Dozat & Manning (2017) was independently reinvented by AVICI and CauScale for causal structure learning — validating this connection.

**Top predictive meta-features** (confirming the parsing analogy):
1. `max_pairwise_MI` (24.6%) — strongest pairwise dependency (≈ biaffine arc score)
2. `n_variables` (14.8%) — network size
3. `max_entropy` (9.5%) — variable complexity
4. `max_cramers_v` (6.7%) — strongest association strength

## 🚀 Quick Start

```python
from causal_selection.meta_learner.predictor import predict_best_algorithms
import pandas as pd

# Load your discrete dataset
df = pd.read_csv("my_discrete_data.csv")

# Get top-3 recommendations
result = predict_best_algorithms(df, k=3)
# Prints ranked algorithms with predicted accuracy and confidence
```

## 📁 Project Structure

```
causal_selection/
├── data/
│   ├── generator.py          # Load bnlearn networks, sample data, DAG→CPDAG
│   ├── bif_files/            # 14 bnlearn BIF files (asia through win95pts)
│   └── results/              # Benchmark CSVs: meta-features, SHD matrices
├── discovery/
│   ├── algorithms.py         # 9 algorithm adapters with timeout handling
│   └── evaluator.py          # SHD, F1, Precision, Recall computation
├── features/
│   └── extractor.py          # 34 meta-features across 5 tiers
├── meta_learner/
│   ├── trainer.py            # Multi-Output RF/GBM + LONO-CV evaluation
│   └── predictor.py          # Inference: dataset → top-3 prediction
├── models/
│   ├── meta_learner.pkl      # Trained Random Forest
│   └── scaler.pkl            # Feature scaler
├── benchmark.py              # Full benchmark orchestration
└── run_benchmark.py          # Resumable benchmark runner
```

## 📈 Benchmark Data

- **14 bnlearn networks**: asia, cancer, earthquake, sachs, survey, alarm, barley, child, insurance, mildew, water, hailfinder, hepar2, win95pts
- **116+ dataset configs**: varying sample sizes (250–10,000) × multiple seeds
- **1,000+ algorithm runs**: 9 algorithms × 116 configs with per-algorithm timeout

## 🔧 Dependencies

```
causal-learn>=0.1.4
pgmpy>=0.1.25
scikit-learn>=1.8
pandas
numpy
scipy
joblib
```

## 📚 References

- **Causal-Copilot** (arxiv:2504.13263) — Closest existing algorithm selection system
- **AVICI** (arxiv:2205.12934) — Amortized causal structure learning (biaffine architecture)
- **Dozat & Manning** (arxiv:1611.01734) — Deep Biaffine Attention for dependency parsing
- **SATzilla** (arxiv:1401.2474) — Algorithm selection via meta-learning
- **bnlearn** (bnlearn.com) — Bayesian network benchmark repository

## License

MIT