{
  "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": null,
  "attempt": 1,
  "epoch": 1,
  "gold": {
    "effect": 0.018,
    "effect_as_stored": "0.018",
    "is_significant": 1,
    "source_id": "causcibench_real.gold.q152",
    "std_error": 0.007,
    "std_error_as_stored": "0.007"
  },
  "label": null,
  "native_id": 152,
  "precision_limited": false,
  "record": {
    "dataset_path": "data/csv_files/realdata/chi_improving.csv",
    "effect": 0.018,
    "method": "rdd",
    "name": "Improving balance in regression discontinuity design by matching: Estimating the effect of academic probation after the first year of college",
    "query": "What is the effect of being placed on academic probation after the first year on the probability that a student leaves school?",
    "result": {
      "final_result": {
        "causal_effect": null,
        "covariates": null,
        "method": null,
        "outcome_variable": null,
        "standard_deviation": null,
        "treatment_variable": null
      },
      "retries": null
    }
  },
  "scores": {
    "control_overlap": null,
    "effect_correct": null,
    "method_correct": null,
    "outcome_match": null,
    "relative_error_pct": null,
    "treatment_match": null
  },
  "status": "failed",
  "task_id": "causcibench_real.q152"
}