auto_causal/methods/propensity_score/diagnostics.py

# 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