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Eden, explain the causal_inference_engine_1765535810 capability
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This capability handles Counterfactual reasoning and intervention planning. Here's the implementation:

```python
import logging
from typing import Dict, List, Optional

class CausalInferenceEngine:
    """
    Causal inference engine for counterfactual analysis
    
    Args:
        interventions: List of possible interventions
        outcomes: Historical outcomes
    """

    def __init__(self):
        self.interventions = []
        self.outcomes = {}
        self.confounders = []
        self.controlled_variables = []

    def add_intervention(self, intervention_name: str, description: str) -> None:
        """
        Add a possible intervention
        
        Args:
            intervention_name: Name of the intervention
            description: Description of what it entails
        """
        self.interventions.append({
            'name': intervention_name,
            'description': description,
            'counterfactual_outcomes': {}
        })

    def add_outcome(self, treatment: str, control: str) -> None:
        """
        Record observed outcomes from a controlled experiment
        
        Args:
            treatment: Outcome with the intervention
            control: Outcome without the intervention
        """
        self.outcomes[treatment] = treatment
        self.outcomes[control] = control

    def identify_confounders(self, variables: List[str]) -> None:
        """
        Identify confounding variables affecting outcomes
        
        Args:
            variables: List of potential variables
        """
        self.confounders = [v for v in variables if 'confounder' in v.lower()]

    def adjust_for_confounders(self) -> None:
        """
        Control for identified confounders
        """
        if not self.confounders:
            logging.info("No confounders identified")
            return
            
        for intervention in self.interventions:
            adjusted_treatment = intervention['name'] + '_adjusted'
            adjusted_control = intervention['name'] + '_control'
            
            # Hypothetical adjustment lo
```

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