Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
利用多任务神经网络高效、可扩展地估计分布处理效应
We propose a novel multi-task neural network approach for estimating distributional treatment effects (DTE) in randomized experiments. While DTE provides more granular insights into the experiment outcomes over conventional methods focusing on the Average Treatment Effect (ATE), estimating it with regression adjustment methods presents significant challenges. Specifically, precision in the distribution tails suffers due to data imbalance, and computational inefficiencies…