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Workshop: A causal view on dynamical systems

A Balanced Design of Time Series Experiments

Tu Ni · Iavor Bojinov · Jinglong Zhao


Time series experiments are a family of experimental designs on a time series. One experimental unit is sequentially exposed to some version of treatment, stays in the version of treatment for a duration of time, and gets exposed to another version of treatment. While this type of experimental design could handle population interference between units, it typically still needs to account for temporal interference, i.e., a treatment at an earlier period persists in impacting the outcomes of the later periods. Practitioners have widely recognized the applicability of time series experiments, yet prior work typically requires a long duration to gain enough power. In this paper, we propose a novel randomized design that significantly increases the power of such experiments. We prove the theoretical performance of the novel design and verify its superior performance by conducting an extensive simulation study.

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