基于gnn和X-Net融合的ITE估计方法OA北大核心CSTPCD

Causal inference helps people make more rational decision-making plans and has wide applications in fields such as e-commerce and precision medicine,and its performance relies critically on the accurate estimation of Individual Treatment Effect(ITE).The selection bias problem and the sample imbalance problem in the observational data affect the accuracy of the individual treatment effect estimation.For the selection bias problem,existing deep learning methods mainly mitigate it by balancing all the covariates,but balancing the processing-independent noise variables in the covariates can lead to inaccurate estimation of individual treatment effect.For the sample imbalance problem,these methods mainly mitigate it by adding sample weights to the loss function.However,this practice does not effectively improve the accuracy of neural network prediction model.In this paper,we propose a method based on deep representation learning,which jointly induces neural networks to obtain balanced shared representations of non-noise variables in the covariates through gnn and IPM(Integral Probability Metric)networks,and then introduces the X-Net to alleviate the sample imbalance problem.The experimental results on semi-synthetic and real datasets respectively show that our algorithm can improve the accuracy of the model individual treatment effect estimation by mitigating the sample selection bias problem and sample imbalance problem.