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"""
Difference-in-Differences Estimator using DoWhy with Statsmodels fallback.
"""
import logging
import pandas as pd
import numpy as np
from typing import Dict, List, Optional, Any, Tuple
from auto_causal.config import get_llm_client # IMPORT LLM Client Factory
# DoWhy imports (Commented out for simplification)
# from dowhy import CausalModel
# from dowhy.causal_estimators import CausalEstimator
# from dowhy.causal_estimator import CausalEstimate
# Statsmodels import for estimation
import statsmodels.formula.api as smf
# Local imports
from .llm_assist import (
identify_time_variable,
determine_treatment_period,
identify_treatment_group,
interpret_did_results
)
from .diagnostics import validate_parallel_trends # Import diagnostics
# Import from the new utils module
from .utils import create_post_indicator
logger = logging.getLogger(__name__)
# --- Helper functions moved from old file ---
def format_did_results(statsmodels_results: Any, interaction_term_key: str,
validation_results: Dict[str, Any],
method_details: str, parameters: Dict[str, Any]) -> Dict[str, Any]:
'''Formats the DiD results from statsmodels results into a standard dictionary.'''
try:
# Use the interaction_term_key passed directly
effect = float(statsmodels_results.params[interaction_term_key])
stderr = float(statsmodels_results.bse[interaction_term_key])
pval = float(statsmodels_results.pvalues[interaction_term_key])
ci = statsmodels_results.conf_int().loc[interaction_term_key].values.tolist()
ci_lower, ci_upper = float(ci[0]), float(ci[1])
logger.info(f"Extracted effect for '{interaction_term_key}'")
except KeyError:
logger.error(f"Interaction term '{interaction_term_key}' not found in statsmodels results. Available params: {statsmodels_results.params.index.tolist()}")
# Fallback to NaN if term not found
effect, stderr, pval, ci_lower, ci_upper = np.nan, np.nan, np.nan, np.nan, np.nan
except Exception as e:
logger.error(f"Error extracting results from statsmodels object: {e}")
effect, stderr, pval, ci_lower, ci_upper = np.nan, np.nan, np.nan, np.nan, np.nan
# Create a standardized results dictionary
results = {
"effect_estimate": effect,
"standard_error": stderr,
"p_value": pval,
"confidence_interval": [ci_lower, ci_upper],
"diagnostics": validation_results,
"parameters": parameters,
"details": str(statsmodels_results.summary())
}
return results
# Comment out unused DoWhy result formatter
# def format_dowhy_results(estimate: CausalEstimate,
# validation_results: Dict[str, Any],
# parameters: Dict[str, Any]) -> Dict[str, Any]:
# '''Formats the DiD results from DoWhy causal estimate into a standard dictionary.'''
# try:
# # Extract values from DoWhy estimate
# effect = float(estimate.value)
# stderr = float(estimate.get_standard_error()) if hasattr(estimate, 'get_standard_error') else np.nan
# ci_lower, ci_upper = estimate.get_confidence_intervals() if hasattr(estimate, 'get_confidence_intervals') else (np.nan, np.nan)
# # Extract p-value if available, otherwise use NaN
# pval = estimate.get_significance_test_results().get('p_value', np.nan) if hasattr(estimate, 'get_significance_test_results') else np.nan
# # Get available details from estimate
# details = str(estimate)
# if hasattr(estimate, 'summary'):
# details = str(estimate.summary())
# logger.info(f"Extracted effect from DoWhy estimate: {effect}")
# except Exception as e:
# logger.error(f"Error extracting results from DoWhy estimate: {e}")
# effect, stderr, pval, ci_lower, ci_upper = np.nan, np.nan, np.nan, np.nan, np.nan
# details = f"Error extracting DoWhy results: {e}"
# # Create a standardized results dictionary
# results = {
# "effect_estimate": effect,
# "effect_se": stderr,
# "p_value": pval,
# "confidence_interval": [ci_lower, ci_upper],
# "diagnostics": validation_results,
# "parameters": parameters,
# "details": details,
# "estimator": "dowhy"
# }
# return results
# --- Main `estimate_effect` function ---
def estimate_effect(df: pd.DataFrame, treatment: str, outcome: str,
covariates: List[str],
dataset_description: Optional[str] = None,
query: Optional[str] = None,
**kwargs) -> Dict[str, Any]:
"""Difference-in-Differences estimation using DoWhy with Statsmodels fallback.
Args:
df: Dataset containing causal variables
treatment: Name of treatment variable (or variable indicating treated group)
outcome: Name of outcome variable
covariates: List of covariate names
dataset_description: Optional dictionary describing the dataset
**kwargs: Method-specific parameters (e.g., time_var, group_var, query, llm instance if needed)
Returns:
Dictionary with effect estimate and diagnostics
"""
query = kwargs.get('query_str')
# llm_instance = kwargs.get('llm') # Pass llm if helpers need it
df_processed = df.copy() # Work on a copy
logger.info("Starting DiD estimation using DoWhy with Statsmodels fallback...")
# --- Step 1: Identify Key Variables (using LLM Assist placeholders) ---
# Pass llm_instance to helpers if they are implemented to use it
llm_instance = get_llm_client() # Get llm instance if passed
time_var = kwargs.get('time_variable', identify_time_variable(df_processed, query, dataset_description, llm=llm_instance))
if time_var is None:
raise ValueError("Time variable could not be identified for DiD.")
if time_var not in df_processed.columns:
raise ValueError(f"Identified time variable '{time_var}' not found in DataFrame.")
# Determine the variable that identifies the panel unit (for grouping/FE)
group_var = kwargs.get('group_variable', identify_treatment_group(df_processed, treatment, query, dataset_description, llm=llm_instance))
if group_var is None:
raise ValueError("Group/Unit variable could not be identified for DiD.")
if group_var not in df_processed.columns:
raise ValueError(f"Identified group/unit variable '{group_var}' not found in DataFrame.")
# Check outcome exists before proceeding further
if outcome not in df_processed.columns:
raise ValueError(f"Outcome variable '{outcome}' not found in DataFrame.")
# Determine treatment period start
treatment_period = kwargs.get('treatment_period_start', kwargs.get('treatment_period',
determine_treatment_period(df_processed, time_var, treatment, query, dataset_description, llm=llm_instance)))
# --- Identify the TRUE binary treatment group indicator column ---
treated_group_col_for_formula = None
# Priority 1: Check if the 'treatment' argument itself is a valid binary indicator
if treatment in df_processed.columns and pd.api.types.is_numeric_dtype(df_processed[treatment]):
unique_treat_vals = set(df_processed[treatment].dropna().unique())
if unique_treat_vals.issubset({0, 1}):
treated_group_col_for_formula = treatment
logger.info(f"Using the provided 'treatment' argument '{treatment}' as binary group indicator.")
# Priority 2: Check if a column explicitly named 'group' exists and is binary
if treated_group_col_for_formula is None and 'group' in df_processed.columns and pd.api.types.is_numeric_dtype(df_processed['group']):
unique_group_vals = set(df_processed['group'].dropna().unique())
if unique_group_vals.issubset({0, 1}):
treated_group_col_for_formula = 'group'
logger.info(f"Using column 'group' as binary group indicator.")
# Priority 3: Fallback - Search other columns (excluding known roles and time-related ones)
if treated_group_col_for_formula is None:
logger.warning(f"Provided 'treatment' arg '{treatment}' is not binary 0/1 and no 'group' column found. Searching other columns...")
potential_group_cols = []
# Exclude outcome, time var, unit ID var, and common time indicators like 'post'
excluded_cols = [outcome, time_var, group_var, 'post', 'is_post_treatment', 'did_interaction']
for col_name in df_processed.columns:
if col_name in excluded_cols:
continue
try:
col_data = df_processed[col_name]
# Ensure we are working with a Series
if isinstance(col_data, pd.DataFrame):
if col_data.shape[1] == 1:
col_data = col_data.iloc[:, 0] # Extract the Series
else:
logger.warning(f"Skipping multi-column DataFrame slice for '{col_name}'.")
continue
# Check if the Series can be interpreted as binary 0/1
if not pd.api.types.is_numeric_dtype(col_data) and not pd.api.types.is_bool_dtype(col_data):
continue # Skip non-numeric/non-boolean columns
unique_vals = set(col_data.dropna().unique())
# Simplified check: directly test if unique values are a subset of {0, 1}
if unique_vals.issubset({0, 1}):
logger.info(f" Found potential binary indicator: {col_name}")
potential_group_cols.append(col_name)
except AttributeError as ae:
# Catch attribute errors likely due to unexpected types
logger.warning(f"Attribute error checking column '{col_name}': {ae}. Skipping.")
except Exception as e:
logger.warning(f"Unexpected error checking column '{col_name}' during group ID search: {e}")
if potential_group_cols:
treated_group_col_for_formula = potential_group_cols[0] # Take the first suitable one found
logger.info(f"Using column '{treated_group_col_for_formula}' found during search as binary group indicator.")
else:
# Final fallback: Use the originally identified group_var, but warn heavily
treated_group_col_for_formula = group_var
logger.error(f"CRITICAL WARNING: Could not find suitable binary treatment group indicator. Using '{group_var}', but this is likely incorrect and will produce invalid DiD estimates.")
# --- Final Check ---
if treated_group_col_for_formula not in df_processed.columns:
# This case should ideally not happen with the logic above but added defensively
raise ValueError(f"Determined treatment group column '{treated_group_col_for_formula}' not found in DataFrame.")
if df_processed[treated_group_col_for_formula].nunique(dropna=True) > 2:
logger.warning(f"Selected treatment group column '{treated_group_col_for_formula}' is not binary (has {df_processed[treated_group_col_for_formula].nunique()} unique values). DiD requires binary treatment group.")
# --- Step 2: Create Indicator Variables ---
post_indicator_col = 'post'
if post_indicator_col not in df_processed.columns:
# Create the post indicator if it doesn't exist
df_processed[post_indicator_col] = create_post_indicator(df_processed, time_var, treatment_period)
# Interaction term is treatment group * post
interaction_term_col = 'did_interaction' # Keep explicit interaction term
df_processed[interaction_term_col] = df_processed[treated_group_col_for_formula] * df_processed[post_indicator_col]
# --- Step 3: Validate Parallel Trends (using the group column) ---
parallel_trends_validation = validate_parallel_trends(df_processed, time_var, outcome,
treated_group_col_for_formula, treatment_period, dataset_description)
# Note: The validation result is currently just a placeholder
if not parallel_trends_validation.get('valid', False):
logger.warning("Parallel trends assumption potentially violated (based on placeholder check). Proceeding with estimation, but results may be biased.")
# Add this info to the final results diagnostics
# --- Step 4: Prepare for Statsmodels Estimation ---
# (DoWhy section commented out for simplicity)
# all_common_causes = covariates + [time_var, group_var] # group_var is unit ID
# use_dowhy_estimate = False
# dowhy_estimate = None
# try:
# # Create DoWhy CausalModel
# model = CausalModel(
# data=df_processed,
# treatment=treated_group_col_for_formula, # Use group indicator here
# outcome=outcome,
# common_causes=all_common_causes,
# )
# logger.info("DoWhy CausalModel created for DiD estimation.")
# # Identify estimand
# identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
# logger.info(f"DoWhy identified estimand: {identified_estimand.estimand_type}")
# # Try to estimate using DiD estimator if available in DoWhy
# try:
# logger.info("Attempting to use DoWhy's DiD estimator...")
# # Debug info - print DataFrame info to help diagnose possible issues
# logger.debug(f"DataFrame shape before DoWhy DiD: {df_processed.shape}")
# # ... (rest of DoWhy debug logs commented out) ...
# # Create params dictionary for DoWhy DiD estimator
# did_params = {
# 'time_var': time_var,
# 'treatment_period': treatment_period,
# 'unit_var': group_var
# }
# # Add control variables if available
# if covariates:
# did_params['control_vars'] = covariates
# logger.debug(f"DoWhy DiD params: {did_params}")
# # Try to use DiD estimator from DoWhy (requires recent version of DoWhy)
# if hasattr(model, 'estimate_effect'):
# try:
# # First check if difference_in_differences method is available
# available_methods = model.get_available_effect_estimators() if hasattr(model, 'get_available_effect_estimators') else []
# logger.debug(f"Available DoWhy estimators: {available_methods}")
# if "difference_in_differences" not in str(available_methods):
# logger.warning("'difference_in_differences' estimator not found in available DoWhy estimators. Falling back to statsmodels.")
# else:
# # Try the estimation with more error handling
# logger.info("Calling DoWhy DiD estimator...")
# estimate = model.estimate_effect(
# identified_estimand,
# method_name="difference_in_differences",
# method_params=did_params
# )
# if estimate:
# # Extra check to verify estimate has expected attributes
# if hasattr(estimate, 'value') and not pd.isna(estimate.value):
# dowhy_estimate = estimate
# use_dowhy_estimate = True
# logger.info(f"Successfully used DoWhy's DiD estimator. Effect estimate: {estimate.value}")
# else:
# logger.warning(f"DoWhy's DiD estimator returned invalid estimate: {estimate}. Falling back to statsmodels.")
# else:
# logger.warning("DoWhy's DiD estimator returned None. Falling back to statsmodels.")
# except IndexError as idx_err:
# # Handle specific IndexError that's occurring
# logger.error(f"IndexError in DoWhy DiD estimator: {idx_err}. Check input data structure.")
# # Trace more details about the error
# import traceback
# logger.error(f"Error traceback: {traceback.format_exc()}")
# logger.warning("Falling back to statsmodels due to IndexError in DoWhy.")
# else:
# logger.warning("DoWhy model does not have estimate_effect method. Falling back to statsmodels.")
# except (ImportError, AttributeError) as e:
# logger.warning(f"DoWhy DiD estimator not available or not implemented: {e}. Falling back to statsmodels.")
# except ValueError as ve:
# logger.error(f"ValueError in DoWhy DiD estimator: {ve}. Likely issue with data formatting. Falling back to statsmodels.")
# except Exception as e:
# logger.error(f"Error using DoWhy's DiD estimator: {e}. Falling back to statsmodels.")
# # Add traceback for better debugging
# import traceback
# logger.error(f"Full error traceback: {traceback.format_exc()}")
# except Exception as e:
# logger.error(f"Failed to create DoWhy CausalModel: {e}", exc_info=True)
# # model = None # Set model to None if creation fails
# Create parameters dictionary for formatting results
parameters = {
"time_var": time_var,
"group_var": group_var, # Unit ID
"treatment_indicator": treated_group_col_for_formula, # Group indicator used in formula basis
"post_indicator": post_indicator_col,
"treatment_period_start": treatment_period,
"covariates": covariates,
}
# Group diagnostics for formatting
did_diagnostics = {
"parallel_trends": parallel_trends_validation,
# "placebo_test": run_placebo_test(...)
}
# If DoWhy estimation was successful, use those results (Section Commented Out)
# if use_dowhy_estimate and dowhy_estimate:
# logger.info("Using DoWhy DiD estimation results.")
# parameters["estimation_method"] = "DoWhy Difference-in-Differences"
# # Format the results
# formatted_results = format_dowhy_results(dowhy_estimate, did_diagnostics, parameters)
# else:
# --- Step 5: Use Statsmodels OLS ---
logger.info("Determining Statsmodels OLS formula based on number of time periods...")
num_time_periods = df_processed[time_var].nunique()
interaction_term_key_for_results: str
method_details_str: str
formula: str
if num_time_periods == 2:
logger.info(
f"Number of unique time periods is 2. Using 2x2 DiD formula: "
f"{outcome} ~ {treated_group_col_for_formula} * {post_indicator_col}"
)
# For 2x2 DiD: outcome ~ group * post_indicator
# The interaction term A:B in statsmodels gives the DiD estimate.
formula_core = f"{treated_group_col_for_formula} * {post_indicator_col}"
interaction_term_key_for_results = f"{treated_group_col_for_formula}:{post_indicator_col}"
formula_parts = [formula_core]
main_model_terms = {outcome, treated_group_col_for_formula, post_indicator_col}
if covariates:
filtered_covs = [
c for c in covariates if c not in main_model_terms
]
if filtered_covs:
formula_parts.extend(filtered_covs)
formula = f"{outcome} ~ {' + '.join(formula_parts)}"
parameters["estimation_method"] = "Statsmodels OLS for 2x2 DiD (Group * Post interaction)"
method_details_str = "DiD via Statsmodels 2x2 (Group * Post interaction)"
else: # num_time_periods > 2
logger.info(
f"Number of unique time periods is {num_time_periods} (>2). "
f"Using TWFE DiD formula: {outcome} ~ {interaction_term_col} + C({group_var}) + C({time_var})"
)
# For TWFE: outcome ~ actual_treatment_variable + UnitFE + TimeFE
# actual_treatment_variable is interaction_term_col (e.g., treated_group * post_indicator)
# UnitFE is C(group_var), TimeFE is C(time_var)
formula_parts = [
interaction_term_col,
f"C({group_var})",
f"C({time_var})"
]
interaction_term_key_for_results = interaction_term_col
main_model_terms = {outcome, interaction_term_col, group_var, time_var}
if covariates:
filtered_covs = [
c for c in covariates if c not in main_model_terms
]
if filtered_covs:
formula_parts.extend(filtered_covs)
formula = f"{outcome} ~ {' + '.join(formula_parts)}"
parameters["estimation_method"] = "Statsmodels OLS with TWFE (C() Notation)"
method_details_str = "DiD via Statsmodels TWFE (C() Notation)"
try:
logger.info(f"Using formula: {formula}")
logger.debug(f"Data head for statsmodels:\n{df_processed.head().to_string()}")
logger.debug(f"Regression DataFrame shape: {df_processed.shape}, Columns: {df_processed.columns.tolist()}")
ols_model = smf.ols(formula=formula, data=df_processed)
if group_var not in df_processed.columns:
# This check is mainly for clustering but good to ensure group_var exists.
# For 2x2, group_var (unit ID) might not be in formula but needed for clustering.
raise ValueError(f"Clustering variable '{group_var}' (panel unit ID) not found in regression data.")
logger.debug(f"Clustering standard errors by: {group_var}")
results = ols_model.fit(cov_type='cluster', cov_kwds={'groups': df_processed[group_var]})
logger.info("Statsmodels estimation complete.")
logger.info(f"Statsmodels Results Summary:\n{results.summary()}")
logger.debug(f"Extracting results using interaction term key: {interaction_term_key_for_results}")
parameters["final_formula"] = formula
parameters["interaction_term_coefficient_name"] = interaction_term_key_for_results
formatted_results = format_did_results(results, interaction_term_key_for_results,
did_diagnostics,
method_details=method_details_str,
parameters=parameters)
formatted_results["estimator"] = "statsmodels"
except Exception as e:
logger.error(f"Statsmodels OLS estimation failed: {e}", exc_info=True)
raise ValueError(f"DiD estimation failed (both DoWhy and Statsmodels): {e}")
# --- Add Interpretation --- (Now add interpretation to the formatted results)
try:
# Use the llm_instance fetched earlier
interpretation = interpret_did_results(formatted_results, did_diagnostics, dataset_description, llm=llm_instance)
formatted_results['interpretation'] = interpretation
except Exception as interp_e:
logger.error(f"DiD Interpretation failed: {interp_e}")
formatted_results['interpretation'] = "Interpretation failed."
return formatted_results |