# Simulation Study

Read this page as an imitation and diagnostic validation page. Passing regret
checks is not enough here; reward, value, Q, and coverage gates decide whether
the objects can be read structurally.

IQ-Learn runs on three synthetic cells covering low-dimensional tabular,
high-dimensional neural, and state-only reward settings. Each cell has known
transitions, policy, value, Q function, and counterfactual oracle objects, so
every recovery claim is checked against the truth. The primary cell is
`canonical_low_action`.

The full result generator is
[`run.py`](https://github.com/rawatpranjal/EconIRL/blob/main/validation/estimators/iq_learn/run.py).
It writes the results file
[`iq_learn.json`](https://github.com/rawatpranjal/EconIRL/blob/main/validation/results/iq_learn.json).

```bash
cd /path/to/econirl
PYTHONPATH=src:. python validation/estimators/iq_learn/run.py
```

The estimator passes imitation and counterfactual regret checks on the primary
cell. Reward, value, and Q recovery fail. Low regret on this cell reflects that
the Q-induced policy happens to produce near-oracle welfare under the applied
interventions, not that the reward or value objects are structurally accurate.
The high-dimensional neural cell and state-only cell fail most structural
recovery checks as well.

## Sparse-Support Guard

The sparse-support guard uses a tiny panel with one observed state and one
observed state-action pair (state coverage 0.333, state-action coverage
0.167). Even when all non-coverage metrics pass, the run is not
counterfactual-valid because support checks fail. The guard prevents future
changes from treating small policy or regret numbers as sufficient when the
expert panel does not cover the relevant state-action space.

```bash
PYTHONPATH=src:. python validation/estimators/iq_learn/sparse_support_guard.py
```

Results: [`iq_learn_sparse_support_guard.json`](https://github.com/rawatpranjal/EconIRL/blob/main/validation/results/iq_learn_sparse_support_guard.json).

## Evidence

IQ-Learn is compared against the full structural and IRL rosters on the
[bus engine](../../simulation_studies/rust_bus.md) and
[taxi gridworld](../../simulation_studies/taxi_gridworld.md) pages. See the
[simulation studies index](../../simulation_studies/index.md) for what each
study shows.