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Publications
Publications
  • 2019
  • Article
  • Journal of the American Statistical Association

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

By: Iavor I Bojinov and Neil Shephard
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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.

Keywords

Causality; Nonparametric; Potential Outcomes; Trading Costs; Mathematical Methods

Citation

Bojinov, Iavor I., and Neil Shephard. "Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading." Journal of the American Statistical Association 114, no. 528 (2019): 1665–1682.
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About The Author

Iavor I. Bojinov

Technology and Operations Management
→More Publications

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  • JPMorganChase: Leadership in the Age of GenAI By: Iavor I. Bojinov, Karim R. Lakhani and David Lane
  • Building an AI Factory at Procter & Gamble By: Iavor I Bojinov, Karim R. Lakhani and Alexis Lefort
  • Building a Generative AI-Powered MBA Course Scheduler By: Iavor Bojinov and Annika Hildebrandt
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