论文标题
长期剂量响应曲线的内核方法
Kernel methods for long term dose response curves
论文作者
论文摘要
因果推断中的核心挑战是如何从短期实验数据中推断长期影响，可能是连续作用。它是在人工智能中产生的：连续行动的长期后果可能引起了人们的关注，但是在探索中只能收集短期奖励。对于此估计，称为长期剂量响应曲线，我们提出了基于内核脊回归的简单非参数估计器。通过将短期实验数据的分布与内核嵌入，我们得出了可解释的权重，以推断长期效应。我们的方法允许行动，短期奖励和长期奖励在一般空间中是连续的。它还允许在短期效应和长期效应之间的联系中具有非线性和异质性。我们证明了一致性均匀，非反应误差界反映了数据的有效维度。作为应用程序，我们估计了项目星的长期剂量响应曲线，该计划是一个社交计划，将学生随机分配到各种班级规模。我们将结果扩展到长期反事实分布，证明融合较弱。
A core challenge in causal inference is how to extrapolate long term effects, of possibly continuous actions, from short term experimental data. It arises in artificial intelligence: the long term consequences of continuous actions may be of interest, yet only short term rewards may be collected in exploration. For this estimand, called the long term dose response curve, we propose a simple nonparametric estimator based on kernel ridge regression. By embedding the distribution of the short term experimental data with kernels, we derive interpretable weights for extrapolating long term effects. Our method allows actions, short term rewards, and long term rewards to be continuous in general spaces. It also allows for nonlinearity and heterogeneity in the link between short term effects and long term effects. We prove uniform consistency, with nonasymptotic error bounds reflecting the effective dimension of the data. As an application, we estimate the long term dose response curve of Project STAR, a social program which randomly assigned students to various class sizes. We extend our results to long term counterfactual distributions, proving weak convergence.
