"""
Pattern 063: Causal Reasoning Agent
Description:
Causal Reasoning enables agents to understand cause-effect relationships,
perform interventions, and reason about counterfactuals. Unlike correlation-based
reasoning, causal reasoning distinguishes between "what happened" and "what would
happen if we changed something", enabling better decision-making and explanation.
Components:
1. Causal Graph: Represents causal relationships between variables
2. Structural Equations: Model mechanisms of causation
3. Intervention Handler: Simulates "do" operations
4. Counterfactual Reasoner: Answers "what if" questions
5. Causal Discovery: Learns causal structure from data
6. Effect Estimation: Quantifies causal effects
Use Cases:
- Root cause analysis
- Decision-making under uncertainty
- Policy evaluation ("what if we change X?")
- Scientific reasoning and hypothesis testing
- Fairness and bias analysis
- Explanation generation
LangChain Implementation:
Implements causal reasoning using LLM-based causal graph construction,
intervention simulation, and counterfactual query answering.
"""
import os
import time
from typing import Dict, Any, List, Optional, Tuple, Set
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
load_dotenv()
class CausalRelationType(Enum):
"""Types of causal relationships"""
DIRECT = "direct" # A → B
INDIRECT = "indirect" # A → C → B
COMMON_CAUSE = "common_cause" # A ← C → B
COLLIDER = "collider" # A → C ← B
BIDIRECTIONAL = "bidirectional" # A ↔ B
@dataclass
class CausalEdge:
"""Edge in causal graph"""
cause: str
effect: str
strength: float = 1.0 # 0.0 to 1.0
mechanism: Optional[str] = None
def __str__(self) -> str:
arrow = "→" if self.strength > 0 else "⊣"
strength_str = f"({self.strength:.2f})" if self.strength != 1.0 else ""
return f"{self.cause} {arrow} {self.effect} {strength_str}"
@dataclass
class CausalGraph:
"""Causal graph structure"""
variables: Set[str] = field(default_factory=set)
edges: List[CausalEdge] = field(default_factory=list)
def add_edge(self, cause: str, effect: str, strength: float = 1.0, mechanism: str = None):
"""Add causal edge"""
self.variables.add(cause)
self.variables.add(effect)
self.edges.append(CausalEdge(cause, effect, strength, mechanism))
def get_parents(self, variable: str) -> List[str]:
"""Get direct causes of variable"""
return [edge.cause for edge in self.edges if edge.effect == variable]
def get_children(self, variable: str) -> List[str]:
"""Get direct effects of variable"""
return [edge.effect for edge in self.edges if edge.cause == variable]
def get_ancestors(self, variable: str) -> Set[str]:
"""Get all ancestors (transitive causes)"""
ancestors = set()
to_visit = self.get_parents(variable)
while to_visit:
parent = to_visit.pop()
if parent not in ancestors:
ancestors.add(parent)
to_visit.extend(self.get_parents(parent))
return ancestors
def to_text(self) -> str:
"""Convert graph to text"""
text = "Causal Graph:\n"
text += f"Variables: {', '.join(sorted(self.variables))}\n\n"
text += "Edges:\n"
for edge in self.edges:
text += f" {edge}\n"
if edge.mechanism:
text += f" Mechanism: {edge.mechanism}\n"
return text
@dataclass
class Intervention:
"""Causal intervention (do-operator)"""
variable: str
value: Any
description: str
@dataclass
class CounterfactualQuery:
"""Counterfactual question"""
observed_facts: Dict[str, Any]
intervention: Intervention
query_variable: str
def to_text(self) -> str:
facts_str = ", ".join(f"{k}={v}" for k, v in self.observed_facts.items())
return f"Given {facts_str}, what if we {self.intervention.description}? What would {self.query_variable} be?"
@dataclass
class CausalAnswer:
"""Answer to causal query"""
query: str
answer: str
confidence: float
reasoning: str
affected_variables: List[str] = field(default_factory=list)
class CausalReasoningAgent:
"""
Agent capable of causal reasoning.
Features:
1. Causal graph construction
2. Intervention simulation
3. Counterfactual reasoning
4. Effect estimation
5. Root cause analysis
"""
def __init__(self, domain: str = "general"):
self.domain = domain
self.causal_graph = CausalGraph()
# LLM for causal discovery
self.discovery_model = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0.3
)
# LLM for reasoning
self.reasoning_model = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0.2
)
def build_causal_graph(self, context: str) -> CausalGraph:
"""Construct causal graph from context"""
prompt = ChatPromptTemplate.from_messages([
("system", f"""You are identifying causal relationships in {self.domain}.
Analyze the context and identify:
1. Variables (factors that can change)
2. Causal relationships (A causes B)
3. Mechanisms (how A causes B)
Format your response as:
VARIABLES:
- variable1
- variable2
...
CAUSAL EDGES:
variable1 → variable2 (strength: 0.0-1.0)
Mechanism: explanation
..."""),
("user", "Context: {context}\n\nCausal Analysis:")
])
chain = prompt | self.discovery_model | StrOutputParser()
analysis = chain.invoke({"context": context})
# Parse response
graph = CausalGraph()
lines = analysis.split('\n')
section = None
current_edge = None
for line in lines:
line = line.strip()
if 'VARIABLES:' in line:
section = 'variables'
elif 'CAUSAL EDGES:' in line or 'EDGES:' in line:
section = 'edges'
elif section == 'variables' and line.startswith('-'):
var = line.lstrip('- ').strip()
if var:
graph.variables.add(var)
elif section == 'edges' and ('→' in line or '->' in line):
# Parse edge
if '→' in line:
parts = line.split('→')
elif '->' in line:
parts = line.split('->')
else:
continue
if len(parts) >= 2:
cause = parts[0].strip()
effect_part = parts[1].strip()
# Extract effect and strength
effect = effect_part.split('(')[0].strip()
strength = 1.0
if '(' in effect_part:
try:
strength_str = effect_part.split('(')[1].split(')')[0]
strength = float(''.join(c for c in strength_str if c.isdigit() or c == '.'))
if strength > 1.0:
strength = strength / 100.0
except:
pass
current_edge = CausalEdge(cause, effect, strength)
graph.edges.append(current_edge)
graph.variables.add(cause)
graph.variables.add(effect)
elif section == 'edges' and 'mechanism:' in line.lower() and current_edge:
mechanism = line.split(':', 1)[1].strip()
current_edge.mechanism = mechanism
self.causal_graph = graph
return graph
def do_intervention(
self,
intervention: Intervention,
context: str
) -> CausalAnswer:
"""Simulate causal intervention (do-operator)"""
# Build/use causal graph
if not self.causal_graph.edges:
self.build_causal_graph(context)
# Find affected variables
affected = {intervention.variable}
affected.update(self.causal_graph.get_children(intervention.variable))
# Recursively add downstream effects
to_check = list(self.causal_graph.get_children(intervention.variable))
while to_check:
var = to_check.pop()
children = self.causal_graph.get_children(var)
for child in children:
if child not in affected:
affected.add(child)
to_check.append(child)
prompt = ChatPromptTemplate.from_messages([
("system", f"""You are performing causal intervention analysis in {self.domain}.
Causal Graph:
{self.causal_graph.to_text()}
The intervention sets {intervention.variable} to {intervention.value}.
This is a CAUSAL intervention (do-operator), not just observation.
Analyze:
1. What changes because of this intervention
2. Which variables are affected and how
3. What are the downstream effects
Provide clear reasoning about the causal chain."""),
("user", """Context: {context}
Intervention: {intervention}
What are the causal effects?""")
])
chain = prompt | self.reasoning_model | StrOutputParser()
analysis = chain.invoke({
"context": context,
"intervention": intervention.description
})
return CausalAnswer(
query=f"do({intervention.variable}={intervention.value})",
answer=analysis,
confidence=0.7,
reasoning="Based on causal graph structure and domain knowledge",
affected_variables=list(affected)
)
def answer_counterfactual(
self,
query: CounterfactualQuery,
context: str
) -> CausalAnswer:
"""Answer counterfactual question"""
# Counterfactuals require:
# 1. What actually happened (observed facts)
# 2. What we change (intervention)
# 3. What we query (counterfactual outcome)
prompt = ChatPromptTemplate.from_messages([
("system", f"""You are answering counterfactual questions in {self.domain}.
Counterfactual reasoning has three steps:
1. Abduction: Infer what must be true given observations
2. Action: Apply the intervention
3. Prediction: Determine outcome in this counterfactual world
Use the causal graph to reason about what would change."""),
("user", """Context: {context}
Observed Facts: {facts}
Counterfactual Intervention: {intervention}
Question: What would {query_var} be in this counterfactual scenario?
Answer:""")
])
chain = prompt | self.reasoning_model | StrOutputParser()
answer = chain.invoke({
"context": context,
"facts": str(query.observed_facts),
"intervention": query.intervention.description,
"query_var": query.query_variable
})
return CausalAnswer(
query=query.to_text(),
answer=answer,
confidence=0.6, # Counterfactuals are harder
reasoning="Counterfactual reasoning via abduction-action-prediction"
)
def identify_root_cause(
self,
effect: str,
context: str
) -> CausalAnswer:
"""Identify root cause of an effect"""
if not self.causal_graph.edges:
self.build_causal_graph(context)
# Get all ancestors
ancestors = self.causal_graph.get_ancestors(effect)
prompt = ChatPromptTemplate.from_messages([
("system", f"""You are performing root cause analysis in {self.domain}.
Causal Graph:
{self.causal_graph.to_text()}
Identify the ROOT CAUSE(S) of the observed effect.
Root causes are variables with:
1. No upstream causes (or only external causes)
2. Strong causal connection to the effect
3. Actionable or controllable
Trace back through the causal chain."""),
("user", """Context: {context}
Observed Effect: {effect}
Potential Causes: {ancestors}
What is the root cause?""")
])
chain = prompt | self.reasoning_model | StrOutputParser()
analysis = chain.invoke({
"context": context,
"effect": effect,
"ancestors": ", ".join(ancestors) if ancestors else "None identified"
})
return CausalAnswer(
query=f"Root cause of: {effect}",
answer=analysis,
confidence=0.75,
reasoning="Traced causal chain to root variables"
)
def demonstrate_causal_reasoning():
"""Demonstrate Causal Reasoning Agent pattern"""
print("=" * 80)
print("PATTERN 063: CAUSAL REASONING AGENT DEMONSTRATION")
print("=" * 80)
print("\nUnderstanding cause-effect relationships and counterfactuals\n")
# Test 1: Building causal graph
print("\n" + "=" * 80)
print("TEST 1: Causal Graph Construction")
print("=" * 80)
agent = CausalReasoningAgent(domain="business")
context1 = """
A company has been experiencing declining sales. Analysis shows:
- Marketing budget was reduced
- Competitor launched new product
- Customer satisfaction scores dropped
- Website traffic decreased
- Sales team morale is low
The reduced marketing budget led to less advertising, which decreased website
traffic. The competitor's new product attracted customers, reducing market share.
Lower sales affected team morale.
"""
print(f"\n📊 Analyzing business scenario...")
graph = agent.build_causal_graph(context1)
print(f"\n🔗 Discovered Causal Graph:")
print(f" Variables: {len(graph.variables)}")
print(f" Edges: {len(graph.edges)}")
print(f"\n{graph.to_text()}")
# Test 2: Intervention analysis
print("\n" + "=" * 80)
print("TEST 2: Causal Intervention (do-operator)")
print("=" * 80)
intervention = Intervention(
variable="marketing budget",
value="increase by 50%",
description="increase marketing budget by 50%"
)
print(f"\n🎯 Intervention: {intervention.description}")
print(f" Question: What causal effects will this have?")
result = agent.do_intervention(intervention, context1)
print(f"\n💡 Causal Effects:")
print(f" Affected Variables: {', '.join(result.affected_variables)}")
print(f"\n Analysis:")
for line in result.answer.split('\n')[:10]: # First 10 lines
if line.strip():
print(f" {line}")
# Test 3: Counterfactual reasoning
print("\n" + "=" * 80)
print("TEST 3: Counterfactual Reasoning")
print("=" * 80)
counterfactual = CounterfactualQuery(
observed_facts={
"marketing_budget": "reduced",
"sales": "declined",
"market_share": "lost"
},
intervention=Intervention(
variable="marketing budget",
value="maintained at original level",
description="maintained marketing budget at original level"
),
query_variable="sales"
)
print(f"\n❓ Counterfactual Question:")
print(f" {counterfactual.to_text()}")
cf_result = agent.answer_counterfactual(counterfactual, context1)
print(f"\n🔮 Counterfactual Answer:")
print(f" Confidence: {cf_result.confidence:.2%}")
print(f"\n Answer:")
for line in cf_result.answer.split('\n')[:8]:
if line.strip():
print(f" {line}")
# Test 4: Root cause analysis
print("\n" + "=" * 80)
print("TEST 4: Root Cause Analysis")
print("=" * 80)
agent2 = CausalReasoningAgent(domain="software systems")
context2 = """
A web application is experiencing slow response times:
- Database queries are taking longer
- Server CPU usage is high
- Memory usage increased
- Number of active users grew
- Cache hit rate decreased
- Recent code deployment added complex queries
The code deployment introduced inefficient queries, which increased
database load. Growing user base put additional load on the system.
High server load caused slower query execution. Poor cache performance
meant more database hits.
"""
print(f"\n🔍 Analyzing system performance issue...")
agent2.build_causal_graph(context2)
root_cause = agent2.identify_root_cause("slow response times", context2)
print(f"\n🎯 Root Cause Analysis:")
print(f" Effect: slow response times")
print(f" Confidence: {root_cause.confidence:.2%}")
print(f"\n Root Cause(s):")
for line in root_cause.answer.split('\n'):
if line.strip():
print(f" {line}")
# Test 5: Comparing correlation vs causation
print("\n" + "=" * 80)
print("TEST 5: Correlation vs Causation")
print("=" * 80)
agent3 = CausalReasoningAgent(domain="healthcare")
context3 = """
Study observations:
- People who drink coffee have lower rates of heart disease
- People who exercise regularly drink more coffee
- People who exercise have better heart health
- Coffee consumption and exercise are correlated
- Income level affects both coffee drinking and exercise
"""
print(f"\n📈 Analyzing correlation pattern...")
graph3 = agent3.build_causal_graph(context3)
print(f"\n Causal Graph:")
for edge in graph3.edges:
print(f" {edge}")
# Compare two interventions
print(f"\n Comparing interventions:")
int1 = Intervention("coffee consumption", "increase", "increase coffee consumption")
int2 = Intervention("exercise", "increase", "increase exercise")
result1 = agent3.do_intervention(int1, context3)
result2 = agent3.do_intervention(int2, context3)
print(f"\n If we increase COFFEE consumption:")
print(f" Affected: {', '.join(result1.affected_variables)}")
print(f"\n If we increase EXERCISE:")
print(f" Affected: {', '.join(result2.affected_variables)}")
print(f"\n ⚠️ Causal vs Correlational insight:")
print(f" Correlation doesn't imply causation!")
print(f" The causal structure reveals true mechanisms.")
# Summary
print("\n" + "=" * 80)
print("CAUSAL REASONING AGENT PATTERN SUMMARY")
print("=" * 80)
print("""
Key Benefits:
1. True Understanding: Distinguish causation from correlation
2. Better Decisions: Predict intervention outcomes
3. Counterfactual Reasoning: Answer "what if" questions
4. Root Cause Analysis: Find true sources of problems
5. Robust Generalization: Transfer across contexts
Causal Hierarchy (Pearl's Ladder):
1. Association (Seeing): P(Y|X)
- "What is?" (observation)
- Correlations and patterns
- Machine learning operates here
2. Intervention (Doing): P(Y|do(X))
- "What if?" (action)
- Effects of interventions
- Experiments and A/B tests
3. Counterfactuals (Imagining): P(Y_x|X',Y')
- "What if I had?" (retrospection)
- Alternative histories
- Requires full causal model
Core Concepts:
- Causal Graph: Directed graph of cause-effect
- Structural Equations: Mechanisms of causation
- do-Operator: Intervention (breaking incoming edges)
- Backdoor Criterion: Identifying confounders
- Frontdoor Criterion: Alternative identification
- Counterfactual: Alternative possible worlds
Types of Causal Questions:
1. Association: "Are X and Y related?"
2. Intervention: "What if we do X?"
3. Counterfactual: "What if we had done X?"
4. Effect Size: "How much does X affect Y?"
5. Attribution: "Did X cause this Y?"
6. Explanation: "Why did Y happen?"
Causal Graph Patterns:
- Chain: A → B → C (mediation)
- Fork: A ← B → C (common cause/confounder)
- Collider: A → B ← C (selection bias)
- Confounding: A ← C → Y (spurious correlation)
Intervention vs Observation:
- Observation: P(Y|X=x) - passive seeing
- Intervention: P(Y|do(X=x)) - active doing
- Key difference: Intervention breaks incoming edges
Use Cases:
- Medicine: Treatment effect estimation
- Policy: Evaluate policy interventions
- Business: Understand driver of metrics
- Science: Hypothesis testing
- Fairness: Identify discrimination
- Debugging: Root cause analysis
- Legal: Attribution and liability
Causal Discovery Methods:
1. Constraint-Based: Test conditional independencies
2. Score-Based: Search for best-fitting graph
3. Functional: Exploit noise structure
4. Hybrid: Combine approaches
5. LLM-Based: Extract from text/knowledge
Challenges:
1. Unobserved Confounders: Hidden common causes
2. Selection Bias: Collider conditioning
3. Measurement Error: Imperfect variables
4. Feedback Loops: Bidirectional causation
5. Time Delays: Lag between cause and effect
6. Context Dependence: Different mechanisms
Best Practices:
1. Draw causal graph explicitly
2. Identify confounders
3. Use randomization when possible
4. Combine domain knowledge + data
5. Test causal assumptions
6. Estimate uncertainty
7. Consider alternative graphs
Production Considerations:
- Causal graph versioning
- Domain expert validation
- A/B testing for verification
- Sensitivity analysis
- Handling edge cases
- Explanation generation
- Confidence calibration
Advanced Techniques:
1. Causal Effect Estimation
- Instrumental variables
- Difference-in-differences
- Regression discontinuity
- Propensity score matching
2. Mediation Analysis
- Direct vs indirect effects
- Path analysis
3. Time-Series Causality
- Granger causality
- Dynamic causal models
4. Causal Reinforcement Learning
- Credit assignment
- Off-policy evaluation
Comparison with Related Patterns:
- vs. Correlation: Distinguishes cause from association
- vs. Prediction: Explains mechanism, not just predicts
- vs. Counterfactual (simple): Full causal framework
- vs. Analogical: Causal structure vs similarity
Integration with Other Patterns:
- World Models: Causal = mechanism in world model
- Planning: Causal graph guides planning
- Explanation: Causal = better explanations
- Reasoning: Foundation for robust reasoning
Philosophical Note:
Causal reasoning represents understanding of "why" not just
"what". It's the difference between prediction (correlation)
and understanding (causation). Essential for AGI.
The Causal Reasoning Agent pattern enables agents to move
beyond correlation to true causal understanding, supporting
better decisions, explanations, and counterfactual reasoning.
""")
if __name__ == "__main__":
demonstrate_causal_reasoning()
Agentic Design Pattern: Causal Reasoning
Pattern 063: Causal Reasoning – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Causal Reasoning pattern implementation.
Pattern Description
Pattern 063: Causal Reasoning Agent
Description: Causal Reasoning enables agents to understand cause-effect relationships, perform interventions, and reason about counterfactuals. Unlike correlation-based reasoning, causal reasoning distinguishes between “what happened” and “what would happen if we changed something”, enabling better decision-making and explanation.
Components: 1. Causal Graph: Represents causal relationships between variables 2. Structural Equations: Model mechanisms of causation 3. Intervention Handler: Simulates “do” operations 4. Counterfactual Reasoner: Answers “what if” questions 5. Causal Discovery: Learns causal structure from data 6. Effect Estimation: Quantifies causal effects
Use Cases: – Root cause analysis – Decision-making under uncertainty – Policy evaluation (“what if we change X?”) – Scientific reasoning and hypothesis testing – Fairness and bias analysis – Explanation generation
LangChain Implementation: Implements causal reasoning using LLM-based causal graph construction, intervention simulation, and counterfactual query answering.
File Statistics
- Total Lines: 722
- Classes: 7
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 063: Causal Reasoning Agent
Description:
Causal Reasoning enables agents to understand cause-effect relationships,
perform interventions, and reason about counterfactuals. Unlike correlation-based
reasoning, causal reasoning distinguishes between "what happened" and "what would
happen if we changed something", enabling better decision-making and explanation.
Components:
1. Causal Graph: Represents causal relationships between variables
2. Structural Equations: Model mechanisms of causation
3. Intervention Handler: Simulates "do" operations
4. Counterfactual Reasoner: Answers "what if" questions
5. Causal Discovery: Learns causal structure from data
6. Effect Estimation: Quantifies causal effects
Use Cases:
- Root cause analysis
- Decision-making under uncertainty
- Policy evaluation ("what if we change X?")
- Scientific reasoning and hypothesis testing
- Fairness and bias analysis
- Explanation generation
LangChain Implementation:
Implements causal reasoning using LLM-based causal graph construction,
intervention simulation, and counterfactual query answering.
"""
Purpose: This module implements the Causal Reasoning pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import dataclasses.field
import datetime.datetime
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import time
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Set
import typing.Tuple
Purpose: Core Python functionality:
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– datetime.datetime: Date and time handling
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– time: Time-related functions
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Set: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Tuple: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: CausalRelationType
Lines 46-52
Inherits from: Enum
Purpose:
Types of causal relationships
Class: CausalEdge
Lines 56-66
Purpose:
Edge in causal graph
Methods:
__str__(self)
Lines 63-66
Purpose: Str operation.
Parameters:
Returns: str
Class: CausalGraph
Lines 70-111
Purpose:
Causal graph structure
Methods:
add_edge(self, cause, effect, strength, mechanism)
Lines 75-79
Purpose:
Add causal edge
Parameters:
– cause: Cause
– effect: Effect
– strength: Strength
– mechanism: Mechanism
get_parents(self, variable)
Lines 81-83
Purpose:
Get direct causes of variable
Parameters:
– variable: Variable
Returns: <ast.Subscript object at 0x000001A06D68FD10>
get_children(self, variable)
Lines 85-87
Purpose:
Get direct effects of variable
Parameters:
– variable: Variable
Returns: <ast.Subscript object at 0x000001A06D159310>
get_ancestors(self, variable)
Lines 89-100
Purpose:
Get all ancestors (transitive causes)
Parameters:
– variable: Variable
Returns: <ast.Subscript object at 0x000001A06D15BA50>
to_text(self)
Lines 102-111
Purpose:
Convert graph to text
Parameters:
Returns: str
Class: Intervention
Lines 115-119
Purpose:
Causal intervention (do-operator)
Class: CounterfactualQuery
Lines 123-131
Purpose:
Counterfactual question
Methods:
to_text(self)
Lines 129-131
Purpose: To Text operation.
Parameters:
Returns: str
Class: CausalAnswer
Lines 135-141
Purpose:
Answer to causal query
Class: CausalReasoningAgent
Lines 144-410
Purpose:
Agent capable of causal reasoning.
Features:
1. Causal graph construction
2. Intervention simulation
3. Counterfactual reasoning
4. Effect estimation
5. Root cause analysis
Methods:
__init__(self, domain)
Lines 156-170
Purpose: Initialize the class instance with required parameters.
Parameters:
– domain: Domain
build_causal_graph(self, context)
Lines 172-253
Purpose:
Construct causal graph from context
Parameters:
– context: Context
Returns: CausalGraph
do_intervention(self, intervention, context)
Lines 255-314
Purpose:
Simulate causal intervention (do-operator)
Parameters:
– intervention: Intervention
– context: Context
Returns: CausalAnswer
answer_counterfactual(self, query, context)
Lines 316-361
Purpose:
Answer counterfactual question
Parameters:
– query: Input query or question
– context: Context
Returns: CausalAnswer
identify_root_cause(self, effect, context)
Lines 363-410
Purpose:
Identify root cause of an effect
Parameters:
– effect: Effect
– context: Context
Returns: CausalAnswer
Functions
demonstrate_causal_reasoning()
Lines 413-717
Purpose:
Demonstrate Causal Reasoning Agent pattern
Key Concepts Summary
Pattern Overview
The Causal Reasoning pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 062
- Pattern 064
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Causal Reasoning pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 063_causal_reasoning.py
Total Lines: 722
Pattern Category: AI Agent Design Pattern #063
Generated from actual implementation code
For educational and reference purposes
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