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Eden, explain the causal_inference_engine_1765511684 capability
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This capability handles advanced functionality. Here's the implementation:

```python
"""
Causal Inference Engine Module
Purpose: Determine cause-effect relationships from data.
"""

from typing import List, Dict, Any, Optional
import pandas as pd
from scipy.stats import chi2_contingency
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

@dataclasses.dataclass
class CausalEffect:
    """Represents a detected causal effect."""
    cause: str
    effect: str
    strength: float
    confidence: float
    p_value: Optional[float] = None
    
class CausalInferenceEngine:
    def __init__(self):
        self.effects = []
        
    def add_data(self, data: pd.DataFrame) -> bool:
        """Add observational data for analysis."""
        if not isinstance(data, pd.DataFrame):
            raise ValueError("Data must be a pandas DataFrame.")
        self.data = data
        return True
        
    def identify_correlations(self) -> List[Dict[str, Any]]:
        """Identify potential correlations in the data."""
        correlations = []
        for col1 in self.data.columns:
            for col2 in self.data.columns:
                if col1 == col2: continue
                corr, p_val = chi2_contingency(
                    pd.crosstab(self.data[col1], self.data[col2]))
                correlations.append({
                    'variable1': col1,
                    'variable2': col2,
                    'correlation_coefficient': corr,
                    'p_value': p_val
                })
        return sorted(correlations, key=lambda x: -x['correlation_coefficient'])
    
    def control_for_confounders(self, potential_cause: str, 
                                potential_effect: str) -> bool:
        """Check if other variables confound the cause-effect relationship."""
        for var in [potential_cause, potential_effect]:
            if var not in self.data.columns:
                raise ValueError(f"Variable {var} not found in data.")
        # Check with control variables
        controls = [c
```

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