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"""
LLM assistance functions for Instrumental Variable (IV) analysis.
This module provides functions for LLM-based assistance in instrumental variable analysis,
including identifying potential instruments, validating IV assumptions, and interpreting results.
"""
from typing import List, Dict, Any, Optional
import logging
# Imported for type hinting
from langchain.chat_models.base import BaseChatModel
# Import shared LLM helpers
from auto_causal.utils.llm_helpers import call_llm_with_json_output
logger = logging.getLogger(__name__)
def identify_instrument_variable(
df_cols: List[str],
query: str,
llm: Optional[BaseChatModel] = None
) -> List[str]:
"""
Use LLM to identify potential instrumental variables from available columns.
Args:
df_cols: List of column names from the dataset
query: User's causal query text
llm: Optional LLM model instance
Returns:
List of column names identified as potential instruments
"""
if llm is None:
logger.warning("No LLM provided for instrument identification")
return []
prompt = f"""
You are assisting with an instrumental variable analysis.
Available columns in the dataset: {df_cols}
User query: {query}
Identify potential instrumental variable(s) from the available columns based on the query.
The treatment and outcome should NOT be included as instruments.
Return ONLY a valid JSON object with the following structure (no explanations or surrounding text):
{{
"potential_instruments": ["column_name1", "column_name2", ...]
}}
"""
response = call_llm_with_json_output(llm, prompt)
if response and "potential_instruments" in response and isinstance(response["potential_instruments"], list):
# Basic validation: ensure items are strings (column names)
valid_instruments = [item for item in response["potential_instruments"] if isinstance(item, str)]
if len(valid_instruments) != len(response["potential_instruments"]):
logger.warning("LLM returned non-string items in potential_instruments list.")
return valid_instruments
logger.warning(f"Failed to get valid instrument recommendations from LLM. Response: {response}")
return []
def validate_instrument_assumptions_qualitative(
treatment: str,
outcome: str,
instrument: List[str],
covariates: List[str],
query: str,
llm: Optional[BaseChatModel] = None
) -> Dict[str, str]:
"""
Use LLM to provide qualitative assessment of IV assumptions.
Args:
treatment: Treatment variable name
outcome: Outcome variable name
instrument: List of instrumental variable names
covariates: List of covariate variable names
query: User's causal query text
llm: Optional LLM model instance
Returns:
Dictionary with qualitative assessments of exclusion and exogeneity assumptions
"""
default_fail = {
"exclusion_assessment": "LLM Check Failed",
"exogeneity_assessment": "LLM Check Failed"
}
if llm is None:
return {
"exclusion_assessment": "LLM Not Provided",
"exogeneity_assessment": "LLM Not Provided"
}
prompt = f"""
You are assisting with assessing the validity of instrumental variable assumptions.
Treatment variable: {treatment}
Outcome variable: {outcome}
Instrumental variable(s): {instrument}
Covariates: {covariates}
User query: {query}
Assess the core Instrumental Variable (IV) assumptions based *only* on the provided variable names and query context:
1. Exclusion restriction: Plausibility that the instrument(s) affect the outcome ONLY through the treatment.
2. Exogeneity (also called Independence): Plausibility that the instrument(s) are not correlated with unobserved confounders that also affect the outcome.
Provide a brief, qualitative assessment (e.g., 'Plausible', 'Unlikely', 'Requires Domain Knowledge', 'Potentially Violated').
Return ONLY a valid JSON object with the following structure (no explanations or surrounding text):
{{
"exclusion_assessment": "<brief assessment of exclusion restriction>",
"exogeneity_assessment": "<brief assessment of exogeneity assumption>"
}}
"""
response = call_llm_with_json_output(llm, prompt)
if response and isinstance(response, dict) and \
"exclusion_assessment" in response and isinstance(response["exclusion_assessment"], str) and \
"exogeneity_assessment" in response and isinstance(response["exogeneity_assessment"], str):
return response
logger.warning(f"Failed to get valid assumption assessment from LLM. Response: {response}")
return default_fail
def interpret_iv_results(
results: Dict[str, Any],
diagnostics: Dict[str, Any],
llm: Optional[BaseChatModel] = None
) -> str:
"""
Use LLM to interpret IV results in natural language.
Args:
results: Dictionary of estimation results (e.g., effect_estimate, p_value, confidence_interval)
diagnostics: Dictionary of diagnostic test results (e.g., first_stage_f_statistic, overid_test)
llm: Optional LLM model instance
Returns:
String containing natural language interpretation of results
"""
if llm is None:
return "LLM was not available to provide interpretation. Please review the numeric results manually."
# Construct a concise summary of inputs for the prompt
results_summary = {}
effect = results.get('effect_estimate')
if effect is not None:
try:
results_summary['Effect Estimate'] = f"{float(effect):.3f}"
except (ValueError, TypeError):
results_summary['Effect Estimate'] = 'N/A (Invalid Format)'
else:
results_summary['Effect Estimate'] = 'N/A'
p_value = results.get('p_value')
if p_value is not None:
try:
results_summary['P-value'] = f"{float(p_value):.3f}"
except (ValueError, TypeError):
results_summary['P-value'] = 'N/A (Invalid Format)'
else:
results_summary['P-value'] = 'N/A'
ci = results.get('confidence_interval')
if ci is not None and isinstance(ci, (list, tuple)) and len(ci) == 2:
try:
results_summary['Confidence Interval'] = f"[{float(ci[0]):.3f}, {float(ci[1]):.3f}]"
except (ValueError, TypeError):
results_summary['Confidence Interval'] = 'N/A (Invalid Format)'
else:
# Handle cases where CI is None or not a 2-element list/tuple
results_summary['Confidence Interval'] = str(ci) if ci is not None else 'N/A'
if 'treatment_variable' in results:
results_summary['Treatment'] = results['treatment_variable']
if 'outcome_variable' in results:
results_summary['Outcome'] = results['outcome_variable']
diagnostics_summary = {}
f_stat = diagnostics.get('first_stage_f_statistic')
if f_stat is not None:
try:
diagnostics_summary['First-Stage F-statistic'] = f"{float(f_stat):.2f}"
except (ValueError, TypeError):
diagnostics_summary['First-Stage F-statistic'] = 'N/A (Invalid Format)'
else:
diagnostics_summary['First-Stage F-statistic'] = 'N/A'
if 'weak_instrument_test_status' in diagnostics:
diagnostics_summary['Weak Instrument Test'] = diagnostics['weak_instrument_test_status']
overid_p = diagnostics.get('overid_test_p_value')
if overid_p is not None:
try:
diagnostics_summary['Overidentification Test P-value'] = f"{float(overid_p):.3f}"
diagnostics_summary['Overidentification Test Applicable'] = diagnostics.get('overid_test_applicable', 'N/A')
except (ValueError, TypeError):
diagnostics_summary['Overidentification Test P-value'] = 'N/A (Invalid Format)'
diagnostics_summary['Overidentification Test Applicable'] = diagnostics.get('overid_test_applicable', 'N/A')
else:
# Explicitly state if not applicable or not available
if diagnostics.get('overid_test_applicable') == False:
diagnostics_summary['Overidentification Test'] = 'Not Applicable'
else:
diagnostics_summary['Overidentification Test P-value'] = 'N/A'
diagnostics_summary['Overidentification Test Applicable'] = diagnostics.get('overid_test_applicable', 'N/A')
prompt = f"""
You are assisting with interpreting instrumental variable (IV) analysis results.
Estimation results summary: {results_summary}
Diagnostic test results summary: {diagnostics_summary}
Explain these Instrumental Variable (IV) results in clear, concise language (2-4 sentences).
Focus on:
1. The estimated causal effect (magnitude, direction, statistical significance based on p-value < 0.05).
2. The strength of the instrument(s) (based on F-statistic, typically > 10 indicates strength).
3. Any implications from other diagnostic tests (e.g., overidentification test suggesting instrument validity issues if p < 0.05).
Return ONLY a valid JSON object with the following structure (no explanations or surrounding text):
{{
"interpretation": "<your concise interpretation text>"
}}
"""
response = call_llm_with_json_output(llm, prompt)
if response and isinstance(response, dict) and \
"interpretation" in response and isinstance(response["interpretation"], str):
return response["interpretation"]
logger.warning(f"Failed to get valid interpretation from LLM. Response: {response}")
return "LLM interpretation could not be generated. Please review the numeric results manually." |