We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimation, is attributed to covariates that affect both treatments and outcome. The presence of high-dimensional co-variates exacerbates the impact of bias as it is harder to isolate and measure the impact of these confounders. In the presence of high-cardinality treatment variables, CI is rendered ill-posed due to the increase in the number of counterfactual outcomes to be predicted. We propose Hi-CI, a deep neural network (DNN) based framework for estimating causal effects in the presence of large number of covariates, and high-cardinal and continuous treatment variables. The proposed architecture comprises of a decorrelation network and an outcome prediction network. In the decorrelation network, we learn a data representation in lower dimensions as compared to the original covariates and addresses confounding bias alongside. Subsequently, in the outcome prediction network, we learn an embedding of high-cardinality and continuous treatments, jointly with the data representation. We demonstrate the efficacy of causal effect prediction of the proposed Hi-CI network using synthetic and real-world NEWS datasets.
翻译:在由高维共变和高基治疗组成的观测研究中,我们用因果推断(CI)来解决反事实回归问题。归结偏差导致不准确的治疗效果估计,归结于影响治疗和结果的共变现象。高维共变现象的存在加剧了偏见的影响,因为较难分离和测量这些同化者的影响。在高心错乱治疗变量存在的情况下,CI因预测反事实结果数量的增加而变得错误。我们提议Hi-CI,一个基于深度神经网络(DNN)的框架,用于在大量共变和高心错觉和连续治疗变量存在的情况下估计因果关系。拟议的结构包括一个分解网络和一个结果预测网络。在变异关系网络中,我们从较低的层面了解到数据代表性,与最初的共变异性相比,并消除了相互融合的偏差。随后在结果预测网络中,我们学习了高心错觉网络(DNNN)基础框架,用于在大量共变和高心和连续治疗变量存在的情况下估计因果关系。我们共同展示了高心错和合成网络数据。