Post-Estimation Diagnostics

After a fit converges, confirm the standard errors, inspect the implied behavior, and run the counterfactuals the estimator supports.

Use this page after fitting, not as a substitute for pre-estimation checks. A clean optimizer return only says the numerical routine stopped; it does not by itself establish identification or counterfactual validity.

Standard errors

The structural estimators report asymptotic standard errors. The se_method argument selects the covariance estimator.

Read standard errors as uncertainty around the stated estimator target. They do not fix a misspecified reward, thin action support, or a missing normalization.

`se_method`	Covariance
`asymptotic`	Inverse observed-information matrix.
`robust`	Sandwich form, the default.
`clustered`	Sandwich clustered by individual for panel dependence.
`bootstrap`	Pairs-cluster resampling over individuals.

model = NFXP(n_states=90, discount=0.9999, se_method="clustered")
model.fit(df, state="mileage_bin", action="replaced", id="bus_id")
print(model.se_)

Inspect the fit

Read the fitted policy, value function, and the implied choice probabilities, and compare them against the observed action frequencies.

This is a behavior check. A close fit to observed choices is necessary, but it is weaker than recovering the reward object.

print(model.summary())
proba = model.predict_proba(states)

Counterfactuals

Change a structural primitive and read the new policy or value. Counterfactuals are only as credible as the identification behind the parameters, so confirm the pre-estimation checks first.

cf = model.counterfactual(RC=4.0)
print(cf.policy[50, 1])

For the standard against which a replication is judged, see the replications ledger.