Spaces:
Running
Running
File size: 13,510 Bytes
1721aea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 |
"""
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
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
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")
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"
)
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")
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")
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")
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")
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" |