# `causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots`

Residual diagnostic plots for multi-treatment nuisance models g_k and m_k.

## Module Contents

### Functions

`plot_residual_diagnostics` | Plot residual diagnostics for multi-treatment nuisance models.

### Data

`__all__` |

### API

causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots.plot_residual_diagnostics(estimate: causalis.data_contracts.multicausal_estimate.MultiCausalEstimate, data: Optional[causalis.data_contracts.multicausaldata.MultiCausalData] = None, *, clip_propensity: float = 1e-06, n_bins: int = 20, marker_size: float = 12.0, alpha: float = 0.35, figsize: Tuple[float, float] = (14.0, 4.8), dpi: int = 220, font_scale: float = 1.1, save: Optional[str] = None, save_dpi: Optional[int] = None, transparent: bool = False) → matplotlib.pyplot.Figure

  Plot residual diagnostics for multi-treatment nuisance models.

  Panels

  1..K. Arm-specific residual-vs-fitted: `u_k = y - g_k` vs `g_k` within arm `D_k=1`. K+1. Binned calibration error for each arm: `E[D_k - m_k | m_k in bin]` vs binned `m_k`.

causalis.scenarios.multi_unconfoundedness.refutation.score.residual_plots.__all__

  [‘plot_residual_diagnostics’]