# `causalis.scenarios.multi_unconfoundedness.refutation.unconfoundedness.unconfoundedness_validation`

Unconfoundedness diagnostics for multi-treatment settings.

## Module Contents

### Functions

`run_unconfoundedness_diagnostics` | Run multi-treatment unconfoundedness diagnostics from data and estimate.
`validate_unconfoundedness_balance` | Convenience wrapper returning the balance block only.

### Data

`__all__` |

### API

causalis.scenarios.multi_unconfoundedness.refutation.unconfoundedness.unconfoundedness_validation.run_unconfoundedness_diagnostics(data: causalis.data_contracts.multicausaldata.MultiCausalData, estimate: causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, *, threshold: float = 0.1, normalize: Optional[bool] = None, return_summary: bool = True) → Dict[str, Any]

  Run multi-treatment unconfoundedness diagnostics from data and estimate.

  This implementation computes pairwise balance between baseline treatment 0 and each active treatment k for either ATE or ATTE estimates.

causalis.scenarios.multi_unconfoundedness.refutation.unconfoundedness.unconfoundedness_validation.validate_unconfoundedness_balance(data: causalis.data_contracts.multicausaldata.MultiCausalData, estimate: causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, *, threshold: float = 0.1, normalize: Optional[bool] = None) → Dict[str, Any]

  Convenience wrapper returning the balance block only.

causalis.scenarios.multi_unconfoundedness.refutation.unconfoundedness.unconfoundedness_validation.__all__

  [‘run_unconfoundedness_diagnostics’, ‘validate_unconfoundedness_balance’]