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METHOD AND SYSTEM FOR CAUSAL INFERENCE IN PRESENCE OF HIGH-DIMENSIONAL COVARIATES AND HIGH-CARDINALITY TREATMENTS
METHOD AND SYSTEM FOR CAUSAL INFERENCE IN PRESENCE OF HIGH-DIMENSIONAL COVARIATES AND HIGH-CARDINALITY TREATMENTS
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机译:在高维协调因子和高基主治疗的情况下因因果推断的方法和系统
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摘要
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. Embodiments herein provide a method and system for CI in presence of high-dimensional covariates and high-cardinality treatments using Hi-CI DNN architecture comprising Hi-CI DNN model built by concatenating a decorrelation network and a modified regression network for jointly generating low-dimensional decorrelated covariates from the high-dimensional covariates, and predicting a set of outcomes for the input data set having the high-cardinality treatments comprising of the plurality of dosage levels by generating per-dosage level embedding to learn representation of the high-cardinality treatments.
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