Zhexiao Lin, Peng Ding
arXiv 26 Oct 2025 · Statistics — Methodology
arXiv:2510.22864 · PDF · Extracted main text
Time-series experiments, also called switchback experiments or N-of-1 trials, play increasingly important roles in modern applications in medical and industrial areas. Under the potential outcomes framework, recent research has studied time-series experiments from the design-based perspective, relying solely on the randomness in the design to drive the statistical inference. Focusing on simpler statistical methods, we examine the design-based properties of regression-based methods for estimating treatment effects in time-series experiments. We demonstrate that the treatment effects of interest can be consistently estimated using ordinary least squares with an appropriately specified working model and transformed regressors. Our analysis allows for estimating a diverging number of treatment effects simultaneously, and establishes the consistency and asymptotic normality of the regression-based estimators. Additionally, we show that asymptotically, the heteroskedasticity and autocorrelation consistent variance estimators provide conservative estimates of the true, design-based variances. Importantly, although our approach relies on regression, our design-based framework allows for misspecification of the regression model.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bojinov, I. and Shephard, N (2019) Time series experiments and causal estimands: exact randomization tests and trading | 1.000 | 14 | 4 | 100% |
| 2 | Gao, M. and Ding, P (2023) Causal inference in network experiments: regression-based analysis and design-based properties self | 0.928 | 5 | 3 | 80% |
| 3 | Liang, T. and Recht, B (2025) Randomization inference when n equals one | 0.874 | 5 | 2 | 100% |
| 4 | Aronow, P. M. and Samii, C (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.644 | 2 | 2 | 100% |
| 5 | Greene, W. H (2003) Econometric analysis | 0.644 | 2 | 2 | 100% |
| 6 | Leung, M. P (2022) Causal inference under approximate neighborhood interference | 0.644 | 2 | 2 | 100% |
| 7 | Lin, W (2013) Agnostic notes on regression adjustments to experimental data: Reexamining freedman's critique | 0.644 | 2 | 2 | 100% |
| 8 | Ni, T., Bojinov, I., and Zhao, J (2023) Design of panel experiments with spatial and temporal interference | 0.644 | 2 | 2 | 100% |
| 9 | Chandrasekhar, A. G., Jackson, M. O., McCormick, T. H., and Thiyages… (2023) General covariance-based conditions for central limit theorems with dependent triangular arrays | 0.511 | 4 | 2 | 25% |
| 10 | Chen, L. H. and Shao, Q.-M (2004) Normal approximation under local dependence | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | When are time series predictions causal? The potential system and dynamic causal effects | 0.737 | 3 | 2 |
| 2 | Calibrated Horizon-Weighted Local Projection Designs for Markov Switchbacks | 0.644 | 2 | 2 |
| 3 | Randomization Tests in Switchback Experiments | 0.511 | 2 | 1 |
| 4 | Design-Based Inference for Time-Series GMM | 0.405 | 1 | 1 |