cascade.core.CausalNetwork.explain

CausalNetwork.explain(x, r, s, l, x_, r_, s_, l_)

Explanation pass of the model

Parameters:

• x (Tensor) – Factual data ([n_particles,] batch_size, n_vars)

• r (Tensor) – Factual intervention regime ([n_particles,] batch_size, n_vars)

• s (Tensor) – Factual covariates (batch_size, n_covariates)

• l (Tensor) – Factual library size (batch_size, 1)

• x_ (Tensor) – Counterfactual data ([n_particles,] batch_size, n_vars)

• r_ (Tensor) – Counterfactual intervention regime ([n_particles,] batch_size, n_vars)

• s_ (Tensor) – Counterfactual covariates (batch_size, n_covariates)

• l_ (Tensor) – Counterfactual library size (batch_size, 1)

Return type:

tuple[Tensor, ...]

Returns:

• Prediction with all factual components

• Prediction with only the counterfactual intervention scaling and bias

• Prediction with only the counterfactual covariates

• Prediction with only the counterfactual latent variable

• Prediction with the counterfactual value of each parent variable

• Prediction with all counterfactual components