Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading
时间序列实验与因果目标量：精确随机化检验及交易应用
We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization-based p-values for testing causal effects, without imposing stringent assumptions. We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects. Finally, we…