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"""
LLM assistance functions for Difference in Means analysis.
"""
from typing import Dict, Any, Optional
import logging
# Imported for type hinting
from langchain.chat_models.base import BaseChatModel
from statsmodels.regression.linear_model import RegressionResultsWrapper
# Import shared LLM helpers
from auto_causal.utils.llm_helpers import call_llm_with_json_output
logger = logging.getLogger(__name__)
def interpret_dim_results(
results: RegressionResultsWrapper,
diagnostics: Dict[str, Any],
treatment_var: str,
llm: Optional[BaseChatModel] = None
) -> str:
"""
Use LLM to interpret Difference in Means results.
Args:
results: Fitted statsmodels OLS results object (from outcome ~ treatment).
diagnostics: Dictionary of diagnostic results (group stats).
treatment_var: Name of the treatment variable.
llm: Optional LLM model instance.
Returns:
String containing natural language interpretation.
"""
default_interpretation = "LLM interpretation not available for Difference in Means."
if llm is None:
logger.info("LLM not provided for Difference in Means interpretation.")
return default_interpretation
try:
# --- Prepare summary for LLM ---
results_summary = {}
diag_details = diagnostics.get('details', {})
control_stats = diag_details.get('control_group_stats', {})
treated_stats = diag_details.get('treated_group_stats', {})
effect = results.params.get(treatment_var)
pval = results.pvalues.get(treatment_var)
results_summary['Effect Estimate (Difference in Means)'] = f"{effect:.3f}" if isinstance(effect, (int, float)) else str(effect)
results_summary['P-value'] = f"{pval:.3f}" if isinstance(pval, (int, float)) else str(pval)
try:
conf_int = results.conf_int().loc[treatment_var]
results_summary['95% Confidence Interval'] = f"[{conf_int[0]:.3f}, {conf_int[1]:.3f}]"
except KeyError:
results_summary['95% Confidence Interval'] = "Not Found"
except Exception as ci_e:
results_summary['95% Confidence Interval'] = f"Error ({ci_e})"
results_summary['Control Group Mean Outcome'] = f"{control_stats.get('mean', 'N/A'):.3f}" if isinstance(control_stats.get('mean'), (int, float)) else str(control_stats.get('mean'))
results_summary['Treated Group Mean Outcome'] = f"{treated_stats.get('mean', 'N/A'):.3f}" if isinstance(treated_stats.get('mean'), (int, float)) else str(treated_stats.get('mean'))
results_summary['Control Group Size'] = control_stats.get('count', 'N/A')
results_summary['Treated Group Size'] = treated_stats.get('count', 'N/A')
# --- Construct Prompt ---
prompt = f"""
You are assisting with interpreting Difference in Means results, likely from an RCT.
Results Summary:
{results_summary}
Explain these results in 1-3 concise sentences. Focus on:
1. The estimated average treatment effect (magnitude, direction, statistical significance based on p-value < 0.05).
2. Compare the mean outcomes between the treated and control groups.
Return ONLY a valid JSON object with the following structure (no explanations or surrounding text):
{{
"interpretation": "<your concise interpretation text>"
}}
"""
# --- Call LLM ---
response = call_llm_with_json_output(llm, prompt)
# --- Process Response ---
if response and isinstance(response, dict) and \
"interpretation" in response and isinstance(response["interpretation"], str):
return response["interpretation"]
else:
logger.warning(f"Failed to get valid interpretation from LLM for Difference in Means. Response: {response}")
return default_interpretation
except Exception as e:
logger.error(f"Error during LLM interpretation for Difference in Means: {e}")
return f"Error generating interpretation: {e}"
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