Abstract: Implementations of the present disclosure build a Bayesian student network using the knowledge learnt by an accurate but complex pre-trained teacher network, and sparsity induced by the variational parameters in a student network. Further, the sparsity inducing capability of the teacher on the student network is learnt by employing a Block Sparse Regularizer on a concatenated tensor of teacher and student network weights. Specifically, the student network is trained using the variational lower bound based loss function, constrained on the hint from the teacher, and block-sparsity of weights.

Abstract: In presence of high-cardinality treatment variables, number of counterfactual outcomes to be estimated is much larger than number of factual observations, rendering the problem to be ill-posed. Furthermore, lack of information regarding the confounders among large number of covariates pose challenges in handling confounding bias. Essential is to find lower-dimensional manifold where an equivalent problem of causal inference can be posed, and counterfactual outcomes can be computed.

Abstract: Causality is a crucial paradigm in several domains where observational data is available. Primary goal of Causal Inference (CI) is to uncover cause-effect relationship between entities. Conventional methods face challenges in providing an accurate CI framework due to cofounding and selection bias in multiple treatment scenario. The present disclosure computes a Propensity Score (PS) from a received CI data for the plurality of subjects under test for a treatment. A Generalized Propensity Score (GPS) is computed for a plurality of treatments corresponding to the plurality of subjects by using the PS. Further, a plurality of task batches are created using the GPS and given as input to the DNN for training. Errors in factual data and in balancing representation of the DNN are rectified using a novel loss function. The trained DNN is further used for predicting the counter factual treatment response corresponding to the factual treatment data.

Abstract: Implementations of the present disclosure build a Bayesian student network using the knowledge learnt by an accurate but complex pre-trained teacher network, and sparsity induced by the variational parameters in a student network. Further, the sparsity inducing capability of the teacher on the student network is learnt by employing a Block Sparse Regularizer on a concatenated tensor of teacher and student network weights. Specifically, the student network is trained using the variational lower bound based loss function, constrained on the hint from the teacher, and block-sparsity of weights.