Title: Time series experiments and causal estimands: exact randomization tests and trading,
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 some of these estimands and exact randomization based
p-values for testing causal effects, without imposing stringent assumptions. 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.