Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading

Iavor Bojinov and Neil Shephard

Journal of the American Statistical Association, 2019, vol. 114, issue 528, 1665-1682

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. Supplementary materials for this article are available online.

Date: 2019
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Citations: View citations in EconPapers (27)

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DOI: 10.1080/01621459.2018.1527225

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