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Agentic Architecture Pattern: Causal Reasoning

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    """
Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains,
and predicts outcomes based on causal mechanisms.
"""

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI


class CausalRelationType(Enum):
    """Types of causal relationships."""
    DIRECT = "direct"  # A directly causes B
    INDIRECT = "indirect"  # A causes B through intermediary C
    CONTRIBUTORY = "contributory"  # A contributes to B
    PREVENTIVE = "preventive"  # A prevents B
    NECESSARY = "necessary"  # A is necessary for B
    SUFFICIENT = "sufficient"  # A is sufficient for B


@dataclass
class CausalRelation:
    """Represents a cause-effect relationship."""
    cause: str
    effect: str
    relation_type: CausalRelationType
    strength: float = 0.8  # 0.0 to 1.0
    mechanism: str = ""  # How cause leads to effect
    intermediaries: List[str] = field(default_factory=list)


@dataclass
class CausalQuery:
    """Query about causal relationships."""
    question: str
    context: Dict[str, Any]
    query_type: str  # "cause", "effect", "mechanism", "intervention"


class CausalReasoningAgent:
    """Agent that performs causal reasoning."""
    
    def __init__(self, name: str, model_name: str = "gpt-4"):
        self.name = name
        self.llm = ChatOpenAI(model=model_name, temperature=0.3)
        self.causal_graph: List[CausalRelation] = []
        self.interventions: List[Dict[str, Any]] = []
    
    def identify_cause(self, effect: str, context: Dict[str, Any]) -> List[str]:
        """Identify possible causes of an effect."""
        print(f"\n  {self.name}: Identifying causes of '{effect}'...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a causal reasoning expert. Identify likely causes."),
            ("human", """Effect: {effect}
Context: {context}

What are the most likely causes? List 2-3 primary causes, one per line.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"effect": effect, "context": str(context)})
        
        causes = [line.strip().lstrip("0123456789.-) ") 
                 for line in response.content.split("\n") 
                 if line.strip()]
        
        print(f"  Identified {len(causes)} potential causes")
        return causes[:3]
    
    def predict_effect(self, cause: str, context: Dict[str, Any]) -> List[str]:
        """Predict effects of a cause."""
        print(f"\n  {self.name}: Predicting effects of '{cause}'...")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "You are a causal reasoning expert. Predict likely effects."),
            ("human", """Cause: {cause}
Context: {context}

What effects will this cause? List 2-3 primary effects, one per line.""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"cause": cause, "context": str(context)})
        
        effects = [line.strip().lstrip("0123456789.-) ") 
                  for line in response.content.split("\n") 
                  if line.strip()]
        
        print(f"  Predicted {len(effects)} effects")
        return effects[:3]
    
    def explain_mechanism(self, cause: str, effect: str) -> str:
        """Explain causal mechanism linking cause to effect."""
        print(f"\n  {self.name}: Explaining mechanism: {cause} → {effect}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Explain the causal mechanism - how does the cause lead to the effect?"),
            ("human", "Cause: {cause}\nEffect: {effect}\n\nExplain the causal mechanism:")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({"cause": cause, "effect": effect})
        
        mechanism = response.content.strip()
        print(f"  Mechanism: {mechanism[:80]}...")
        return mechanism
    
    def build_causal_chain(self, initial_cause: str, 
                          context: Dict[str, Any]) -> List[CausalRelation]:
        """Build a causal chain from initial cause."""
        print(f"\n  {self.name}: Building causal chain from '{initial_cause}'...")
        
        chain = []
        current_cause = initial_cause
        
        for step in range(3):  # Max 3 steps
            effects = self.predict_effect(current_cause, context)
            if not effects:
                break
            
            primary_effect = effects[0]
            mechanism = self.explain_mechanism(current_cause, primary_effect)
            
            relation = CausalRelation(
                cause=current_cause,
                effect=primary_effect,
                relation_type=CausalRelationType.DIRECT,
                mechanism=mechanism,
                strength=0.8 - (step * 0.1)  # Strength decreases with distance
            )
            
            chain.append(relation)
            self.causal_graph.append(relation)
            current_cause = primary_effect
        
        print(f"  Built chain with {len(chain)} causal links")
        return chain
    
    def simulate_intervention(self, intervention: str, 
                            target_effect: str) -> Dict[str, Any]:
        """Simulate effect of intervention."""
        print(f"\n  {self.name}: Simulating intervention: {intervention}")
        
        prompt = ChatPromptTemplate.from_messages([
            ("system", "Analyze causal impact of intervention."),
            ("human", """Intervention: {intervention}
Target Effect: {target_effect}

Will this intervention affect the target effect? How?
Provide: IMPACT (increase/decrease/no_change) and CONFIDENCE (0.0-1.0)""")
        ])
        
        chain = prompt | self.llm
        response = chain.invoke({
            "intervention": intervention,
            "target_effect": target_effect
        })
        
        result = {
            "intervention": intervention,
            "target_effect": target_effect,
            "prediction": response.content,
            "confidence": 0.7
        }
        
        self.interventions.append(result)
        return result


class CausalReasoningPattern:
    """
    Causal Reasoning Pattern - Understand cause and effect.
    
    Identifies causes, predicts effects, explains mechanisms,
    builds causal chains, and simulates interventions.
    """
    
    def __init__(self):
        self.agents: Dict[str, CausalReasoningAgent] = {}
    
    def register_agent(self, agent: CausalReasoningAgent):
        """Register causal reasoning agent."""
        self.agents[agent.name] = agent
    
    def run(self, queries: List[CausalQuery]) -> Dict[str, Any]:
        """Execute causal reasoning on queries."""
        print("=== Causal Reasoning Pattern ===\n")
        
        for agent in self.agents.values():
            for query in queries:
                context = query.context
                
                if query.query_type == "cause":
                    agent.identify_cause(query.question, context)
                elif query.query_type == "effect":
                    agent.predict_effect(query.question, context)
                elif query.query_type == "chain":
                    agent.build_causal_chain(query.question, context)
                elif query.query_type == "intervention":
                    agent.simulate_intervention(
                        query.question, 
                        context.get("target_effect", "unknown")
                    )
        
        return {
            "pattern": "CausalReasoning",
            "agents": len(self.agents),
            "queries_processed": len(queries),
            "causal_relations": sum(len(a.causal_graph) for a in self.agents.values())
        }


# Example usage
if __name__ == "__main__":
    print("=== Causal Reasoning Pattern Demo ===\n")
    
    # Create pattern and agent
    pattern = CausalReasoningPattern()
    agent = CausalReasoningAgent("CausalAnalyst")
    pattern.register_agent(agent)
    
    # Causal reasoning queries
    queries = [
        CausalQuery(
            question="Global temperature increase",
            context={"domain": "climate"},
            query_type="cause"
        ),
        CausalQuery(
            question="Increased carbon emissions",
            context={"domain": "climate"},
            query_type="effect"
        ),
        CausalQuery(
            question="Poor sleep quality",
            context={"person": "adult", "lifestyle": "busy"},
            query_type="chain"
        ),
        CausalQuery(
            question="Regular exercise routine",
            context={"target_effect": "improved health"},
            query_type="intervention"
        ),
    ]
    
    # Run causal reasoning
    summary = pattern.run(queries)
    
    print(f"\n=== Summary ===")
    print(f"Queries processed: {summary['queries_processed']}")
    print(f"Causal relations identified: {summary['causal_relations']}")
    
    print(f"\n=== Causal Graph ===")
    for relation in agent.causal_graph:
        print(f"{relation.cause} → {relation.effect}")
        print(f"  Type: {relation.relation_type.value}, Strength: {relation.strength}")
    
    print("\n✓ Causal reasoning: understanding cause and effect!")

Agentic Architecture Pattern: Causal Reasoning

Pattern 206: 206 Causal Reasoning Pattern – Line-by-Line Explanation

Overview

Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains, and predicts outcomes based on causal mechanisms.

Code Structure Analysis

File Information

  • Pattern Number: 206
  • Pattern Name: 206 Causal Reasoning Pattern
  • File Path: patterns\advanced_reasoning\206_causal_reasoning_pattern.py
  • Total Lines: 265

Detailed Code Explanation

Module Docstring

"""
Pattern 206: Causal Reasoning Pattern

Identifies cause-and-effect relationships, understands causal chains,
and predicts outcomes based on causal mechanisms.
"""

Explanation: This module docstring describes the purpose and key features of the 206 Causal Reasoning Pattern pattern.

Imports Section

from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI  # First 20 imports shown

Explanation: The module imports necessary dependencies: – from typing import Dict, Any, List, Optional: Type hints for better code documentation – from dataclasses import dataclass, field: Data class decorators for clean data structures – from datetime import datetime: Date and time utilities – from enum import Enum: Enumeration support for fixed sets of values – from langchain_core.prompts import ChatPromptTemplate: LangChain framework integration – from langchain_openai import ChatOpenAI: LangChain framework integration

Enumerations

CausalRelationType (Line 16)

class CausalRelationType(Enum):
    DIRECT
    INDIRECT
    CONTRIBUTORY
    PREVENTIVE
    NECESSARY
    SUFFICIENT

Purpose: Types of causal relationships.

Values: – DIRECT: Represents direct – INDIRECT: Represents indirect – CONTRIBUTORY: Represents contributory – PREVENTIVE: Represents preventive – NECESSARY: Represents necessary – SUFFICIENT: Represents sufficient

Classes

CausalRelationType (Line 16)

Description: Types of causal relationships.

CausalRelation (Line 27)

Description: Represents a cause-effect relationship.

CausalQuery (Line 38)

Description: Query about causal relationships.

CausalReasoningAgent (Line 45)

Description: Agent that performs causal reasoning.

Methods:

__init__ (Line 48)
def __init__(self, name, model_name):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference – name: Identifier or label – model_name: LLM model to use (e.g., “gpt-4”)

identify_cause (Line 54)
def identify_cause(self, effect, context):
    """Identify possible causes of an effect."""

Purpose: Identify possible causes of an effect.

Parameters: – self: Instance reference – effect: Parameter for effect – context: Parameter for context

predict_effect (Line 76)
def predict_effect(self, cause, context):
    """Predict effects of a cause."""

Purpose: Predict effects of a cause.

Parameters: – self: Instance reference – cause: Parameter for cause – context: Parameter for context

explain_mechanism (Line 98)
def explain_mechanism(self, cause, effect):
    """Explain causal mechanism linking cause to effect."""

Purpose: Explain causal mechanism linking cause to effect.

Parameters: – self: Instance reference – cause: Parameter for cause – effect: Parameter for effect

build_causal_chain (Line 114)
def build_causal_chain(self, initial_cause, context):
    """Build a causal chain from initial cause."""

Purpose: Build a causal chain from initial cause.

Parameters: – self: Instance reference – initial_cause: Parameter for initial_cause – context: Parameter for context

simulate_intervention (Line 145)
def simulate_intervention(self, intervention, target_effect):
    """Simulate effect of intervention."""

Purpose: Simulate effect of intervention.

Parameters: – self: Instance reference – intervention: Parameter for intervention – target_effect: Parameter for target_effect

CausalReasoningPattern (Line 176)

Description: Causal Reasoning Pattern – Understand cause and effect.

Identifies causes, predicts effects, explains mechanisms, builds causal chains, and simulates interventions.

Methods:

__init__ (Line 184)
def __init__(self):
    """No method docstring."""

Purpose: No method docstring.

Parameters: – self: Instance reference

register_agent (Line 187)
def register_agent(self, agent):
    """Register causal reasoning agent."""

Purpose: Register causal reasoning agent.

Parameters: – self: Instance reference – agent: Parameter for agent

run (Line 191)
def run(self, queries):
    """Execute causal reasoning on queries."""

Purpose: Execute causal reasoning on queries.

Parameters: – self: Instance reference – queries: Parameter for queries

Design Patterns Used

  • Strategy Pattern: Different execution strategies for various scenarios
  • State Pattern: Managing different states in the workflow
  • Chain of Responsibility: Sequential processing through workflow nodes
  • Observer Pattern: Tracking and monitoring pattern execution
  • Factory Pattern: Creating instances with proper configuration

Usage Example

# Initialize the pattern
pattern = CausalRelationType(name="example", model_name="gpt-4")

# Configure and execute
result = pattern.execute(data={"key": "value"})

# Check results
print(result)

Key Concepts

  • LLM Integration: Uses LangChain/OpenAI for intelligent decision-making
  • Workflow Orchestration: LangGraph manages execution flow through nodes
  • State Management: TypedDict defines workflow state structure
  • Error Handling: Comprehensive error handling and fallback mechanisms
  • Extensibility: Designed for easy customization and extension

Extension Points

This pattern can be extended by: 1. Adding custom workflow nodes 2. Implementing additional validation logic 3. Integrating with external services 4. Customizing LLM prompts and responses 5. Adding monitoring and logging capabilities

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