{
  "task_id": "F6-conf-04",
  "naive_estimate": -0.13169790632300882,
  "naive_ci_low": -0.1603518326928757,
  "naive_ci_high": -0.10304397995314195,
  "naive_se": 0.014619618827634278,
  "baseline_metric": 3.8601022336733584,
  "model_metric": 1.9758571828823268,
  "effect_estimate": 0.25353820594501253,
  "effect_se": 0.009812212326345536,
  "effect_se_type": "HC1 heteroskedasticity-robust",
  "effect_ci_low": 0.2343066231767153,
  "effect_ci_high": 0.27276978871330976,
  "skill_index_coefficient": -4.624620879345756,
  "skill_index_se": 0.07816357249116873,
  "conclusion": "positive_effect",
  "feasibility": "complete",
  "feasibility_reason": null,
  "data_issues": [
    {
      "issue": "12 rows have negative training_hours (impossible values)",
      "handling": "excluded from main analysis; sensitivity estimate on all 1200 rows reported (sensitivity_all_rows_effect)"
    },
    {
      "issue": "strong confounding: skill_index correlates 0.69 with training_hours and -0.79 with productivity_growth",
      "handling": "adjusted for pre-programme skill_index by OLS"
    }
  ],
  "n_input_rows": {
    "firms.csv": 1200
  },
  "n_analysed": 1188,
  "random_seed": null,
  "sensitivity_all_rows_effect": 0.25222449623309495,
  "sensitivity_all_rows_se": 0.00947885217664965,
  "sensitivity_quadratic_skill_effect": 0.25383792381516485,
  "sensitivity_quadratic_skill_se": 0.009844473118691516,
  "corr_skill_training": 0.6775720280598007
}