# LLM Integration points for Propensity Score methods
import pandas as pd
from typing import List, Optional, Dict, Any
[docs]
def determine_optimal_caliper(df: pd.DataFrame, treatment: str,
covariates: List[str],
query: Optional[str] = None) -> float:
'''Determines optimal caliper for PSM using data or LLM.
Placeholder: Returns a default value.
'''
# TODO: Implement data-driven (e.g., based on PS distribution) or LLM-assisted caliper selection.
# Common rule of thumb is 0.2 * std dev of logit(PS), but that requires calculating PS first.
return 0.2
[docs]
def determine_optimal_weight_type(df: pd.DataFrame, treatment: str,
query: Optional[str] = None) -> str:
'''Determines the optimal type of IPW weights (ATE, ATT, etc.).
Placeholder: Defaults to ATE.
'''
# TODO: Implement LLM or rule-based selection.
return "ATE"
[docs]
def determine_optimal_trim_threshold(df: pd.DataFrame, treatment: str,
propensity_scores: Optional[pd.Series] = None,
query: Optional[str] = None) -> Optional[float]:
'''Determines optimal threshold for trimming extreme propensity scores.
Placeholder: Defaults to no trimming (None).
'''
# TODO: Implement data-driven or LLM-assisted threshold selection (e.g., based on score distribution).
return None # Corresponds to no trimming by default
# Placeholder for calling LLM to get parameters (can use the one in utils if general enough)
[docs]
def get_llm_parameters(df: pd.DataFrame, query: str, method: str) -> Dict[str, Any]:
'''Placeholder to get parameters via LLM based on dataset and query.'''
# In reality, call something like analyze_dataset_for_method from utils.llm_helpers
print(f"Simulating LLM call to get parameters for {method}...")
if method == "PS.Matching":
return {"parameters": {"caliper": 0.15}, "validation": {"check_balance": True}}
elif method == "PS.Weighting":
return {"parameters": {"weight_type": "ATE", "trim_threshold": 0.05}, "validation": {"check_weights": True}}
else:
return {"parameters": {}, "validation": {}}