Eval 01: Parameter Recovery

Validates that neural networks recover the true structural parameters θ(x) = [α(x), β(x)] across all 12 families.

Configuration

Parameter	Value
n	5000
epochs	200
Seed	42
Families	9 tested

Results

Family	RMSE(α)	RMSE(β)	Corr(α)	Corr(β)	Status
linear	0.036	0.045	0.994	0.998	PASS
gaussian	0.030	0.040	0.994	0.998	PASS
logit	0.127	0.180	0.963	0.968	PASS
poisson	0.014	0.030	0.998	0.972	PASS
negbin	0.059	0.061	0.985	0.938	PASS
gamma	0.039	0.028	0.997	0.999	PASS
weibull	0.860	0.007	1.000	1.000	PASS
gumbel	0.063	0.063	0.975	0.999	PASS
tobit	0.042	0.024	0.999	0.998	PASS

Overall: 9/9 PASS (all Corr(β) > 0.93)

Key Findings

• All families achieve Corr(β) > 0.93 with oracle heterogeneity

• Binary outcome models (logit, probit) are harder but still pass

• Count models (poisson, negbin) recover parameters well

• Continuous positive models (gamma, weibull) achieve near-perfect recovery

Pass Criteria

• Corr(α) > 0.90

• Corr(β) > 0.90

Run Command

python3 -m evals.eval_01_theta 2>&1 | tee evals/reports/eval_01_$(date +%Y%m%d_%H%M%S).txt