@article {546276, title = {Time series experiments and causal estimands: exact randomization tests and trading}, journal = {Journal of the American Statistical Association}, volume = {114}, year = {2019}, month = {2019}, pages = {1665-82}, abstract = {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 provide three methods for generalizing our approach to multiple units that are receiving the same class of treatment, over time. \ We test our methodology on simulated "potential autoregressions,"which have a causal interpretation. \ Our methodology is partially inspired by data from a large number of experiments carried out by a financial company who compared the impact of two different ways of trading equity futures contracts. \ We use our methodology to make causal statements about their trading methods.}, author = {Iavor Bojinov and Neil Shephard} }