catenets.models.jax.rnet module

Implements NN based on R-learner and U-learner (as discussed in Nie & Wager (2017))

class RNet(second_stage_strategy: str = 'R', data_split: bool = False, cross_fit: bool = False, n_cf_folds: int = 2, n_layers_out: int = 2, n_layers_r: int = 3, n_layers_out_t: int = 2, n_layers_r_t: int = 3, n_units_out: int = 100, n_units_r: int = 200, n_units_out_t: int = 100, n_units_r_t: int = 200, penalty_l2: float = 0.0001, penalty_l2_t: float = 0.0001, step_size: float = 0.0001, step_size_t: float = 0.0001, n_iter: int = 10000, batch_size: int = 100, n_iter_min: int = 200, val_split_prop: float = 0.3, early_stopping: bool = True, patience: int = 10, n_iter_print: int = 50, seed: int = 42, nonlin: str = 'elu', binary_y: bool = False)

Bases: catenets.models.jax.base.BaseCATENet

Class implements R-learner and U-learner using NNs

Parameters

• second_stage_strategy (str, default 'R') – Which strategy to use in the second stage (‘R’ for R-learner, ‘U’ for U-learner)

• data_split (bool, default False) – Whether to split the data in two folds for estimation

• cross_fit (bool, default False) – Whether to perform cross fitting

• n_cf_folds (int) – Number of crossfitting folds to use

• n_layers_out (int) – First stage Number of hypothesis layers (n_layers_out x n_units_out + 1 x Dense layer)

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

• n_layers_r (int) – First stage Number of representation layers before hypothesis layers (distinction between hypothesis layers and representation layers is made to match TARNet & SNets)

• n_units_r (int) – First stage Number of hidden units in each representation layer

• n_layers_out_t (int) – Second stage Number of hypothesis layers (n_layers_out x n_units_out + 1 x Dense layer)

• n_units_out_t (int) – Second stage Number of hidden units in each hypothesis layer

• n_layers_r_t (int) – Second stage Number of representation layers before hypothesis layers (distinction between hypothesis layers and representation layers is made to match TARNet & SNets)

• n_units_r_t (int) – Second stage Number of hidden units in each representation layer

• penalty_l2 (float) – First stage l2 (ridge) penalty

• penalty_l2_t (float) – Second stage l2 (ridge) penalty

• step_size (float) – First stage learning rate for optimizer

• step_size_t (float) – Second stage 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)

• early_stopping (bool, default True) – Whether to use early stopping

• patience (int) – Number of iterations to wait before early stopping after decrease in validation loss

• n_iter_min (int) – Minimum number of iterations to go through before starting early stopping

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

• seed (int) – Seed used

• nonlin (string, default 'elu') – Nonlinearity to use in NN

_abc_impl = <_abc_data object>

_get_predict_function() → Callable

_get_train_function() → Callable

fit(X: jax._src.basearray.Array, y: jax._src.basearray.Array, w: jax._src.basearray.Array, p: Optional[jax._src.basearray.Array] = None) → catenets.models.jax.rnet.RNet

Fit method for a CATENet. Takes covariates, outcome variable and treatment indicator as input

Parameters

• X (pd.DataFrame or np.array) – Covariate matrix

• y (np.array) – Outcome vector

• w (np.array) – Treatment indicator

• p (np.array) – Vector of (known) treatment propensities. Currently only supported for TwoStepNets.

predict(X: jax._src.basearray.Array, return_po: bool = False, return_prop: bool = False) → jax._src.basearray.Array

Predict treatment effect estimates using a CATENet. Depending on method, can also return potential outcome estimate and propensity score estimate.

Parameters

• X (pd.DataFrame or np.array) – Covariate matrix

• return_po (bool, default False) – Whether to return potential outcome estimate

• return_prop (bool, default False) – Whether to return propensity estimate

Returns

Return type

array of CATE estimates, optionally also potential outcomes and propensity

_train_and_predict_r_stage1(X: jax._src.basearray.Array, y: jax._src.basearray.Array, w: jax._src.basearray.Array, fit_mask: jax._src.basearray.Array, pred_mask: jax._src.basearray.Array, n_layers_out: int = 2, n_units_out: int = 100, n_layers_r: int = 3, n_units_r: int = 200, penalty_l2: float = 0.0001, step_size: float = 0.0001, n_iter: int = 10000, batch_size: int = 100, val_split_prop: float = 0.3, early_stopping: bool = True, patience: int = 10, n_iter_min: int = 200, n_iter_print: int = 50, seed: int = 42, nonlin: str = 'elu', binary_y: bool = False) → Any

train_r_net(X: jax._src.basearray.Array, y: jax._src.basearray.Array, w: jax._src.basearray.Array, p: Optional[jax._src.basearray.Array] = None, second_stage_strategy: str = 'R', data_split: bool = False, cross_fit: bool = False, n_cf_folds: int = 2, n_layers_out: int = 2, n_layers_r: int = 3, n_layers_r_t: int = 3, n_layers_out_t: int = 2, n_units_out: int = 100, n_units_r: int = 200, n_units_out_t: int = 100, n_units_r_t: int = 200, penalty_l2: float = 0.0001, penalty_l2_t: float = 0.0001, step_size: float = 0.0001, step_size_t: float = 0.0001, n_iter: int = 10000, batch_size: int = 100, val_split_prop: float = 0.3, early_stopping: bool = True, patience: int = 10, n_iter_min: int = 200, n_iter_print: int = 50, seed: int = 42, return_val_loss: bool = False, nonlin: str = 'elu', binary_y: bool = False) → Any

train_r_stage2(X: jax._src.basearray.Array, y_ortho: jax._src.basearray.Array, w_ortho: jax._src.basearray.Array, n_layers_out: int = 2, n_units_out: int = 100, n_layers_r: int = 0, n_units_r: int = 200, penalty_l2: float = 0.0001, step_size: float = 0.0001, n_iter: int = 10000, batch_size: int = 100, val_split_prop: float = 0.3, early_stopping: bool = True, patience: int = 10, n_iter_min: int = 200, n_iter_print: int = 50, seed: int = 42, return_val_loss: bool = False, nonlin: str = 'elu', avg_objective: bool = True) → Any