"""
Method validator component for causal inference methods.
This module validates the selected causal inference method against
dataset characteristics and available variables.
"""
from typing import Dict, List, Any, Optional
[docs]
def validate_method(method_info: Dict[str, Any], dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> Dict[str, Any]:
"""
Validate the selected causal method against dataset characteristics.
Args:
method_info: Information about the selected method from decision_tree
dataset_analysis: Dataset analysis results from dataset_analyzer
variables: Identified variables from query_interpreter
Returns:
Dict with validation results:
- valid: Boolean indicating if method is valid
- concerns: List of concerns/issues with the selected method
- alternative_suggestions: Alternative methods if the selected method is problematic
- recommended_method: Updated method recommendation if issues are found
"""
method = method_info.get("selected_method")
assumptions = method_info.get("method_assumptions", [])
# Get required variables
treatment = variables.get("treatment_variable")
outcome = variables.get("outcome_variable")
covariates = variables.get("covariates", [])
time_variable = variables.get("time_variable")
group_variable = variables.get("group_variable")
instrument_variable = variables.get("instrument_variable")
running_variable = variables.get("running_variable")
cutoff_value = variables.get("cutoff_value")
# Initialize validation result
validation_result = {
"valid": True,
"concerns": [],
"alternative_suggestions": [],
"recommended_method": method,
}
# Common validations for all methods
if treatment is None:
validation_result["valid"] = False
validation_result["concerns"].append("Treatment variable is not identified")
if outcome is None:
validation_result["valid"] = False
validation_result["concerns"].append("Outcome variable is not identified")
# Method-specific validations
if method == "propensity_score_matching":
validate_propensity_score_matching(validation_result, dataset_analysis, variables)
elif method == "regression_adjustment":
validate_regression_adjustment(validation_result, dataset_analysis, variables)
elif method == "instrumental_variable":
validate_instrumental_variable(validation_result, dataset_analysis, variables)
elif method == "difference_in_differences":
validate_difference_in_differences(validation_result, dataset_analysis, variables)
elif method == "regression_discontinuity_design":
validate_regression_discontinuity(validation_result, dataset_analysis, variables)
elif method == "backdoor_adjustment":
validate_backdoor_adjustment(validation_result, dataset_analysis, variables)
# If there are serious concerns, recommend alternatives
if not validation_result["valid"]:
validation_result["recommended_method"] = recommend_alternative(
method, validation_result["concerns"], method_info.get("alternatives", [])
)
# Make sure assumptions are listed in the validation result
validation_result["assumptions"] = assumptions
print("--------------------------")
print("Validation result:", validation_result)
print("--------------------------")
return validation_result
[docs]
def validate_propensity_score_matching(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate propensity score matching method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
treatment = variables.get("treatment_variable")
covariates = variables.get("covariates", [])
# Check if treatment is binary using column_categories
is_binary = dataset_analysis.get("column_categories", {}).get(treatment) == "binary"
# Fallback to check if the column has only two unique values (0 and 1)
if not is_binary:
column_types = dataset_analysis.get("column_types", {})
if column_types.get(treatment) == "int64" or column_types.get(treatment) == "int32":
# Assuming int type with only 0s and 1s is binary
is_binary = True
if not is_binary:
validation_result["valid"] = False
validation_result["concerns"].append(
"Treatment variable is not binary, which is required for propensity score matching"
)
# Check if there are sufficient covariates
if len(covariates) < 2:
validation_result["concerns"].append(
"Few covariates identified, which may limit the effectiveness of propensity score matching"
)
# Check for sufficient overlap
variable_relationships = dataset_analysis.get("variable_relationships", {})
treatment_imbalance = variable_relationships.get("treatment_imbalance", 0.5)
if treatment_imbalance < 0.1 or treatment_imbalance > 0.9:
validation_result["concerns"].append(
"Treatment groups are highly imbalanced, which may lead to poor matching quality"
)
validation_result["alternative_suggestions"].append("regression_adjustment")
[docs]
def validate_regression_adjustment(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate regression adjustment method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
outcome = variables.get("outcome_variable")
# Check outcome type for appropriate regression model
outcome_data = dataset_analysis.get("variable_types", {}).get(outcome, {})
outcome_type = outcome_data.get("type")
if outcome_type == "categorical" and outcome_data.get("n_categories", 0) > 2:
validation_result["concerns"].append(
"Outcome is categorical with multiple categories, which may require multinomial regression"
)
# Check for potential nonlinear relationships
nonlinear_relationships = dataset_analysis.get("nonlinear_relationships", False)
if nonlinear_relationships:
validation_result["concerns"].append(
"Potential nonlinear relationships detected, which may require more flexible models"
)
[docs]
def validate_instrumental_variable(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate instrumental variable method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
instrument_variable = variables.get("instrument_variable")
treatment = variables.get("treatment_variable")
if instrument_variable is None:
validation_result["valid"] = False
validation_result["concerns"].append(
"No instrumental variable identified, which is required for this method"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
return
# Check for instrument strength (correlation with treatment)
variable_relationships = dataset_analysis.get("variable_relationships", {})
instrument_correlation = next(
(corr.get("correlation", 0) for corr in variable_relationships.get("correlations", [])
if corr.get("var1") == instrument_variable and corr.get("var2") == treatment
or corr.get("var1") == treatment and corr.get("var2") == instrument_variable),
0
)
if abs(instrument_correlation) < 0.2:
validation_result["concerns"].append(
"Instrument appears weak (low correlation with treatment), which may lead to bias"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
[docs]
def validate_difference_in_differences(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate difference-in-differences method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
time_variable = variables.get("time_variable")
group_variable = variables.get("group_variable")
if time_variable is None:
validation_result["valid"] = False
validation_result["concerns"].append(
"No time variable identified, which is required for difference-in-differences"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
if group_variable is None:
validation_result["valid"] = False
validation_result["concerns"].append(
"No group variable identified, which is required for difference-in-differences"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
# Check for parallel trends
temporal_structure = dataset_analysis.get("temporal_structure", {})
parallel_trends = temporal_structure.get("parallel_trends", False)
if not parallel_trends:
validation_result["concerns"].append(
"No evidence of parallel trends, which is a key assumption for difference-in-differences"
)
validation_result["alternative_suggestions"].append("synthetic_control")
[docs]
def validate_regression_discontinuity(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate regression discontinuity method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
running_variable = variables.get("running_variable")
cutoff_value = variables.get("cutoff_value")
if running_variable is None:
validation_result["valid"] = False
validation_result["concerns"].append(
"No running variable identified, which is required for regression discontinuity"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
if cutoff_value is None:
validation_result["valid"] = False
validation_result["concerns"].append(
"No cutoff value identified, which is required for regression discontinuity"
)
validation_result["alternative_suggestions"].append("propensity_score_matching")
# Check for discontinuity at threshold
discontinuities = dataset_analysis.get("discontinuities", {})
has_discontinuity = discontinuities.get("has_discontinuities", False)
if not has_discontinuity:
validation_result["valid"] = False
validation_result["concerns"].append(
"No clear discontinuity detected at the threshold, which is necessary for this method"
)
validation_result["alternative_suggestions"].append("regression_adjustment")
[docs]
def validate_backdoor_adjustment(validation_result: Dict[str, Any],
dataset_analysis: Dict[str, Any],
variables: Dict[str, Any]) -> None:
"""
Validate backdoor adjustment method requirements.
Args:
validation_result: Current validation result to update
dataset_analysis: Dataset analysis results
variables: Identified variables
"""
covariates = variables.get("covariates", [])
if len(covariates) == 0:
validation_result["valid"] = False
validation_result["concerns"].append(
"No covariates identified for backdoor adjustment"
)
validation_result["alternative_suggestions"].append("regression_adjustment")
[docs]
def recommend_alternative(method: str, concerns: List[str], alternatives: List[str]) -> str:
"""
Recommend an alternative method if the current one has issues.
Args:
method: Current method
concerns: List of concerns with the current method
alternatives: List of alternative methods suggested by the decision tree
Returns:
String with the recommended method
"""
# If there are alternatives, recommend the first one
if alternatives:
return alternatives[0]
# If no alternatives, use regression adjustment as a fallback
if method != "regression_adjustment":
return "regression_adjustment"
# If regression adjustment is also problematic, use propensity score matching
return "propensity_score_matching"