# Contract (P0)

Question: causal effect of one extra training hour per employee (programme year) on next-year productivity growth (pp/hour), simulated firm data. Estimand: linear, homogeneous coefficient on training_hours under selection on observables given skill_index (pre-programme, so a valid non-post-treatment control). Assumptions: no unobserved confounders beyond skill_index, linearity in hours and correct functional form for skill, no interference, skill_index measured without error. Baseline: OLS productivity_growth ~ 1 + training_hours (required unadjusted model). Outputs: out/results.json (naive_estimate, naive_ci_low/high, baseline_metric, model_metric, effect_estimate, effect_se, effect_ci_low/high, skill_index_coefficient, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/figure1.png, out/table1.csv (model, estimate, se, ci_low, ci_high), out/report.md (6 sections), out/analysis.py, out/claims.json. Information rules: only data/firms.csv; unadjusted = OLS with intercept only. Invalidity: missing confounder column, post-treatment control, impossible values driving result, no overlap. skill_index is stated pre-programme -> adjustment feasible.