1[2 {"id":"C1","text":"The unadjusted association between training hours and next-year productivity growth is negative (about -0.13 pp per hour, 95% CI excludes zero).","results_keys":["naive_estimate","naive_ci_low","naive_ci_high"],"status":"supported","caveat":"Association only; confounded by skill_index."},3 {"id":"C2","text":"Adjusting for pre-programme skill_index, one additional training hour per employee raises productivity growth by about 0.25 pp (HC1 95% CI 0.234 to 0.273).","results_keys":["effect_estimate","effect_se","effect_ci_low","effect_ci_high","conclusion"],"status":"supported","caveat":"Causal interpretation relies on selection on observables with a single covariate and linear homogeneous effect; simulated data."},4 {"id":"C3","text":"Higher baseline skill is associated with lower productivity growth, which confounds the naive estimate and explains the sign reversal.","results_keys":["skill_index_coefficient","naive_estimate","effect_estimate"],"status":"supported","caveat":"skill_index coefficient is not itself given a causal interpretation."},5 {"id":"C4","text":"The adjusted model fits much better than the unadjusted model (residual RMSE about 1.98 vs 3.86 pp).","results_keys":["model_metric","baseline_metric"],"status":"supported","caveat":"In-sample fit; better fit does not by itself validate identification."},6 {"id":"C5","text":"12 rows with negative training hours were excluded; the main estimate uses 1188 of 1200 firms and is insensitive to this choice.","results_keys":["data_issues","n_input_rows","n_analysed"],"status":"supported","caveat":"Sensitivity estimates are in table1.csv, not results.json."}7]8