{
  "about": "CauSciBench native output: 'record' has the shape their evaluation/extract.py reads (a run's records in one list rescore with their evaluation/run_evaluation.py); the method is the tool-family label of the answer call (tool-family accuracy from tool calls, not a method the model named); 'scores' are the per-item terms of evaluation/metrics.py.",
  "answer_ref": "call_878d39bc:effect",
  "attempt": 1,
  "epoch": 1,
  "gold": {
    "effect": 0.086,
    "effect_as_stored": "0.086",
    "is_significant": 1,
    "source_id": "causcibench_real.gold.q098",
    "std_error": 0.031,
    "std_error_as_stored": "0.031"
  },
  "label": {
    "family": "rd",
    "method": "rdd",
    "rule": "regression discontinuity",
    "tool_id": "infer.rdd"
  },
  "native_id": 98,
  "precision_limited": false,
  "record": {
    "dataset_path": "data/csv_files/realdata/song_estimating_incumbency.csv",
    "effect": 0.086,
    "method": "rdd",
    "name": "Estimating incumbency effects using regression discontinuity design",
    "query": "In the UK, in very close elections, does just barely winning make it more likely that the party's candidate will stand again as the incumbent in the next election?",
    "result": {
      "final_result": {
        "causal_effect": 0.09871020361757638,
        "covariates": [],
        "method": "rdd",
        "outcome_variable": "win",
        "standard_deviation": 0.016381752687952524,
        "treatment_variable": "l_vmargin"
      },
      "retries": null
    }
  },
  "scores": {
    "control_overlap": 1.0,
    "effect_correct": 0,
    "method_correct": 1,
    "outcome_match": 1,
    "relative_error_pct": 14.779306532065561,
    "treatment_match": null
  },
  "status": "failed",
  "task_id": "causcibench_real.q098"
}