1[2 {"id":"C1","text":"The unadjusted association between training hours and next-year productivity growth is negative (about -0.13 pp/hour, 95% CI excludes zero).","results_keys":["naive_estimate","naive_ci_low","naive_ci_high"],"status":"supported","caveat":"Association only; confounded by baseline skill."},3 {"id":"C2","text":"Adjusting for pre-programme skill_index, one additional training hour per employee raises productivity growth by about 0.25 pp (95% CI 0.23 to 0.27).","results_keys":["effect_estimate","effect_se","effect_ci_low","effect_ci_high","conclusion"],"status":"supported","caveat":"Causal reading relies on untestable selection-on-observables with a single covariate, and on linearity/homogeneity."},4 {"id":"C3","text":"Baseline skill confounds the raw association: it has a strongly negative coefficient on productivity growth (about -4.6 pp per index unit) while being positively correlated with training hours, producing the sign reversal.","results_keys":["skill_index_coefficient","naive_estimate","effect_estimate"],"status":"supported","caveat":"The correlation of skill with training (r≈0.69) is reported in the report, not in results.json."},5 {"id":"C4","text":"The adjusted model fits better, roughly halving residual RMSE (3.86 to 1.98 pp).","results_keys":["baseline_metric","model_metric"],"status":"supported","caveat":"In-sample fit; better fit does not itself establish causal validity."},6 {"id":"C5","text":"Excluding the 12 firms with invalid negative training hours does not materially affect the estimate.","results_keys":["data_issues","n_analysed","n_input_rows"],"status":"supported","caveat":"Sensitivity estimate (0.252) is in table1.csv, not a results.json key."}7]8