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fix RDD selection in CAIS decision tree #37

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fix RDD selection in CAIS decision tree #37

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9 changes: 6 additions & 3 deletions cais/components/decision_tree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,6 +160,7 @@ def select_method(dataset_properties: Dict[str, Any], excluded_methods: Optional
running_var = dataset_properties.get("running_variable")
cutoff_val = dataset_properties.get("cutoff_value")
time_var = dataset_properties.get("time_variable")
group_var = dataset_properties.get("group_variable")
is_rct = dataset_properties.get("is_rct", False)
has_temporal = dataset_properties.get("has_temporal_structure", False)
frontdoor = dataset_properties.get("frontdoor_criterion", False)
Expand Down Expand Up @@ -220,9 +221,9 @@ def add(method: str, justification: str, prio_order: List[str]):
]

# Common early structural signals first (still only add as candidates)
if has_temporal and time_var:
if has_temporal and time_var and group_var:
add(DIFF_IN_DIFF,
f"Temporal structure via '{time_var}'—consider Difference-in-Differences (assumes parallel trends).",
f"Temporal structure via '{time_var}' for state structure via '{group_var}' —consider Difference-in-Differences (assumes parallel trends).",
[DIFF_IN_DIFF]) # highest among itself

if running_var and cutoff_val is not None:
Expand Down Expand Up @@ -361,7 +362,9 @@ def rule_based_select_method(dataset_analysis, variables, is_rct, llm, dataset_d

properties = {"treatment_variable": variables.get("treatment_variable"), "instrument_variable":variables.get("instrument_variable"),
"covariates": variables.get("covariates", []), "outcome_variable": variables.get("outcome_variable"),
"time_variable": variables.get("time_variable"), "running_variable": variables.get("running_variable"),
"time_variable": variables.get("time_variable"),
"group_variable": variables.get("group_variable"),
"running_variable": variables.get("running_variable"),
"treatment_variable_type": variables.get("treatment_variable_type", "binary"),
"has_temporal_structure": dataset_analysis.get("temporal_structure", False).get("has_temporal_structure", False),
"frontdoor_criterion": variables.get("frontdoor_criterion", False),
Expand Down
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