catenets.models.torch.slearner module

class SLearner(n_unit_in: int, binary_y: bool, po_estimator: Optional[Any] = None, n_layers_out: int = 2, n_units_out: int = 100, n_units_out_prop: int = 100, n_layers_out_prop: int = 2, weight_decay: float = 0.0001, lr: float = 0.0001, n_iter: int = 10000, batch_size: int = 100, val_split_prop: float = 0.3, n_iter_print: int = 50, seed: int = 42, nonlin: str = 'elu', weighting_strategy: Optional[str] = None, batch_norm: bool = True, early_stopping: bool = True, dropout: bool = False, dropout_prob: float = 0.2)

Bases: catenets.models.torch.base.BaseCATEEstimator

S-learner for treatment effect estimation (single learner, treatment indicator just another feature).

Parameters

  • n_unit_in (int) – Number of features

  • binary_y (bool) – Whether the outcome is binary

  • po_estimator (sklearn/PyTorch model, default: None) – Custom potential outcome model. If this parameter is set, the rest of the parameters are ignored.

  • n_layers_out (int) – Number of hypothesis layers (n_layers_out x n_units_out + 1 x Linear layer)

  • n_layers_out_prop (int) – Number of hypothesis layers for propensity score(n_layers_out x n_units_out + 1 x Linear layer)

  • n_units_out (int) – Number of hidden units in each hypothesis layer

  • n_units_out_prop (int) – Number of hidden units in each propensity score hypothesis layer

  • weight_decay (float) – l2 (ridge) penalty

  • lr (float) – learning rate for optimizer

  • n_iter (int) – Maximum number of iterations

  • batch_size (int) – Batch size

  • val_split_prop (float) – Proportion of samples used for validation split (can be 0)

  • n_iter_print (int) – Number of iterations after which to print updates

  • seed (int) – Seed used

  • nonlin (string, default 'elu') – Nonlinearity to use in the neural net. Can be ‘elu’, ‘relu’, ‘selu’ or ‘leaky_relu’.

  • weighting_strategy (optional str, None) – Whether to include propensity head and which weightening strategy to use

_backward_hooks: Dict[int, Callable]
_buffers: Dict[str, Optional[torch.Tensor]]
_create_extended_matrices(X: torch.Tensor) → torch.Tensor
_forward_hooks: Dict[int, Callable]
_forward_pre_hooks: Dict[int, Callable]
_is_full_backward_hook: Optional[bool]
_load_state_dict_post_hooks: Dict[int, Callable]
_load_state_dict_pre_hooks: Dict[int, Callable]
_modules: Dict[str, Optional[Module]]
_non_persistent_buffers_set: Set[str]
_parameters: Dict[str, Optional[torch.nn.parameter.Parameter]]
_state_dict_hooks: Dict[int, Callable]
fit(X: torch.Tensor, y: torch.Tensor, w: torch.Tensor) → catenets.models.torch.slearner.SLearner

Fit treatment models.

Parameters

  • X (torch.Tensor of shape (n_samples, n_features)) – The features to fit to

  • y (torch.Tensor of shape (n_samples,) or (n_samples, )) – The outcome variable

  • w (torch.Tensor of shape (n_samples,)) – The treatment indicator

predict(X: torch.Tensor, return_po: bool = False, training: bool = False) → torch.Tensor

Predict treatment effects and potential outcomes

Parameters

X (array-like of shape (n_samples, n_features)) – Test-sample features

Returns

y

Return type

array-like of shape (n_samples,)

training: bool