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# Balance and sensitivity analysis diagnostics for Propensity Score methods
import pandas as pd
import numpy as np
from typing import Dict, List, Optional, Any
# Import necessary plotting libraries if visualizations are needed
# import matplotlib.pyplot as plt
# import seaborn as sns
# Import utility for standardized differences if needed
from auto_causal.methods.utils import calculate_standardized_differences
def assess_balance(df_original: pd.DataFrame, df_matched_or_weighted: pd.DataFrame,
treatment: str, covariates: List[str],
method: str,
propensity_scores_original: Optional[np.ndarray] = None,
propensity_scores_matched: Optional[np.ndarray] = None,
weights: Optional[np.ndarray] = None) -> Dict[str, Any]:
'''Assesses covariate balance before and after matching/weighting.
Placeholder: Returns dummy diagnostic data.
'''
print(f"Assessing balance for {method}...")
# TODO: Implement actual balance checking using standardized differences,
# variance ratios, KS tests, etc.
# Example using standardized differences (needs calculate_standardized_differences):
# std_diff_before = calculate_standardized_differences(df_original, treatment, covariates)
# std_diff_after = calculate_standardized_differences(df_matched_or_weighted, treatment, covariates, weights=weights)
dummy_balance_metric = {cov: np.random.rand() * 0.1 for cov in covariates} # Simulate good balance
return {
"balance_metrics": dummy_balance_metric,
"balance_achieved": True, # Placeholder
"problematic_covariates": [], # Placeholder
# Add plots or paths to plots if generated
"plots": {
"balance_plot": "balance_plot.png",
"overlap_plot": "overlap_plot.png"
}
}
def assess_weight_distribution(weights: np.ndarray, treatment_indicator: pd.Series) -> Dict[str, Any]:
'''Assesses the distribution of IPW weights.
Placeholder: Returns dummy diagnostic data.
'''
print("Assessing weight distribution...")
# TODO: Implement checks for extreme weights, effective sample size, etc.
return {
"min_weight": float(np.min(weights)),
"max_weight": float(np.max(weights)),
"mean_weight": float(np.mean(weights)),
"std_dev_weight": float(np.std(weights)),
"effective_sample_size": len(weights) / (1 + np.std(weights)**2 / np.mean(weights)**2), # Kish's ESS approx
"potential_issues": np.max(weights) > 20 # Example check
}
def plot_overlap(df: pd.DataFrame, treatment: str, propensity_scores: np.ndarray, save_path: str = 'overlap_plot.png'):
'''Generates plot showing propensity score overlap.
Placeholder: Does nothing.
'''
print(f"Generating overlap plot (placeholder) -> {save_path}")
# TODO: Implement actual plotting (e.g., using seaborn histplot or kdeplot)
pass
def plot_balance(balance_metrics_before: Dict[str, float], balance_metrics_after: Dict[str, float], save_path: str = 'balance_plot.png'):
'''Generates plot showing covariate balance before/after.
Placeholder: Does nothing.
'''
print(f"Generating balance plot (placeholder) -> {save_path}")
# TODO: Implement actual plotting (e.g., Love plot)
pass |