公开(公告)号：US20210326727A1

公开(公告)日：2021-10-21

申请号：US17249454

申请日：2021-03-02

Applicant: Tata Consultancy Services Limited

Inventor： Garima GUPTA ,  Ankit SHARMA ,  Ranjitha PRASAD ,  Arnab CHATTERJEE ,  Lovekesh VIG ,  Gautam SHROFF

IPC: G06N5/04 ,  G06N3/04 ,  G06F16/2458

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.