{
  "about": "A prediction in CausalReasoningBenchmark's output schema (vendor/output.schema.json), built from this attempt's answer call, under 'identification_output' (the key evaluate_result.py reads; save it as <index>.json in a predictions folder to rescore it with the benchmark's own script).",
  "answer_figure": {
    "key": "coef.post_ever",
    "ref": "call_3525c2f7:coef.post_ever",
    "tool": "infer.ols"
  },
  "estimation": {
    "ci_jaccard_overlap": 0.999999999999988,
    "ci_overlap": true,
    "estimate_within_solution_ci": true,
    "estimation_error": 3.0531133177191805e-16,
    "gold_ci": [
      0.00012098616695463416,
      0.05104942718255129
    ],
    "null_hypothesis_decision_correct": true,
    "opposite_effect_direction": false,
    "percentage_error": 1.1933119620768745e-12,
    "pred_ci": [
      0.00012098616695446415,
      0.051049427182550844
    ],
    "signed_error": -3.0531133177191805e-16,
    "standard_error_gap": 6.938893903907228e-17,
    "standard_error_gap_pct": 5.340918269025021e-15
  },
  "identification": {
    "causal_quantity_correct": true,
    "controls_correct": true,
    "identification_spec_correct": true,
    "minimal_controlling_set_included": true,
    "outcomes_correct": true,
    "post_treatment_set_excluded": true,
    "strategy_correct": true,
    "strategy_specific_fields_correct": true,
    "treatments_correct": true
  },
  "identification_output": {
    "causal_quantity": "ATT",
    "controls": [],
    "effect_estimate": 0.025585206674752656,
    "group_variable": "ticker",
    "outcomes": [
      "lobby"
    ],
    "standard_error": 0.012991949238672546,
    "strategy": "Difference-in-Differences",
    "time_variable": "year",
    "treatments": [
      "treated"
    ]
  },
  "index": 36,
  "read": {
    "dataset": "ref_panel",
    "factors": {
      "post_ever": [
        "ever",
        "post"
      ]
    },
    "label": {
      "family": "did",
      "group_variable": "ever",
      "rule": "OLS on the interaction of a group indicator and a period indicator (2x2 difference in differences)",
      "time_variable": "post",
      "tool_id": "infer.ols"
    },
    "lineage": {
      "ever": [
        "treated"
      ],
      "n": [
        "lobby"
      ],
      "nyears": [
        "lobby"
      ],
      "post": [
        "year"
      ],
      "post_ever": [
        "treated",
        "year"
      ]
    }
  },
  "rules": "Prediction built from the answer call alone (the call of the answer figure, magi_bench.answer): strategy = labels.py family of (tool, arguments) mapped to CRB's enum (did -> Difference-in-Differences, iv -> Instrumental Variable, rd -> Regression Discontinuity, rct -> RCT, ols/panel_fe -> Conditional Exogeneity), the item's identification.json group/time/running columns being the declared columns the label rule reads; causal_quantity = the conventional estimand of that family (did ATT, iv LATE, rd CATE, otherwise ATE), not stated by the model; treatments = the call's treatment argument (x[0], endog[0], treat, treated, treatment, d; for a product/interaction column its first factor), outcomes = y, controls = the other regressors and covariates minus the design terms (the treatment, the outcome, the factors of an interaction, the unit and time columns); instrument = instruments/z; running_variable and cutoff = infer.rdd's running and cutoff; time_variable and group_variable = the panel tools' time and unit, infer.did_2x2's post and cluster, infer.ols's two fixed effects (unit first) when labelled did; for infer.ols on a group x period interaction (labelled did), group_variable = the call's cluster column (CRB's unit identifier; omitted when the call does not cluster) and time_variable = the source column the period indicator was built from (omitted when it was built from none). Every column is mapped back to the CSV columns it was built from through the dataset's transforms (lineage); a single source column replaces the name, several leave it. is_encouragement_design is never filled: no tool argument states it. effect_estimate and standard_error are the answer figure's value (after answer_format) and SE (scaled by its unit_scale). No --auto-rescale.",
  "schema_errors": [],
  "schema_valid": true,
  "scorer": {
    "auto_rescale": false,
    "evaluate_result_sha256": "e6af71dfc8201edb819276f4986694bd71b73b88eb288898c246a3c16d60166a",
    "output_schema_sha256": "0e01d494e7a46493567fa2ee6c4eef7a7ce93838c24a4d7ba0e76f6b62c2b42e",
    "port": "magi_bench.suites.crb_papers.identification_metrics / estimation_metrics",
    "repo": "https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark",
    "revision": "edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888"
  },
  "task_id": "crb_papers.q036"
}