论文标题
用于后门和前门调整的神经平均嵌入方法
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
论文作者
论文摘要
我们考虑在两种情况下的平均和反事实治疗效果的估计：后门调整和前门调整。在这两种情况下，目标是在不使用隐藏的混杂因素的情况下恢复治疗效果。通过首先估计相关的协变量（“第一阶段”回归），然后以（条件）对此功能作为“第二阶段”程序的期望，可以实现这一目标。我们建议使用回归函数直接计算出这些条件期望，以在第一阶段的学习输入特征上，从而避免对采样或密度估计的需求。所有功能和功能（尤其是第二阶段的输出功能）都是从数据自适应地学习的神经网络，唯一要求第一阶段的最后一层应为线性。所提出的方法显示出融合到真正的因果参数，并且在具有挑战性的因果基准（包括涉及高维图像数据的设置）上优于最新的最新方法。
We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment. The goal in both cases is to recover the treatment effect without having an access to a hidden confounder. This objective is attained by first estimating the conditional mean of the desired outcome variable given relevant covariates (the "first stage" regression), and then taking the (conditional) expectation of this function as a "second stage" procedure. We propose to compute these conditional expectations directly using a regression function to the learned input features of the first stage, thus avoiding the need for sampling or density estimation. All functions and features (and in particular, the output features in the second stage) are neural networks learned adaptively from data, with the sole requirement that the final layer of the first stage should be linear. The proposed method is shown to converge to the true causal parameter, and outperforms the recent state-of-the-art methods on challenging causal benchmarks, including settings involving high-dimensional image data.
