arXiv 20 Mar 2026 · Econometrics
arXiv:2603.20394 · PDF · OpenAlex · Extracted main text
The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time $t$ on an outcome at future time $t+h$, accounting for the presence of features. The potential system provides nonparametric content for, e.g., time series experiments, time series regression, local projection, impulse response functions and SVARs. It closes a gap between time series causality and nonparametric cross-sectional causal methods, and provides a foundation for many new methods which have causal content.
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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 N. Shephard (2019) Time series experiments and causal estimands: exact randomization tests and trading | 1.000 | 5 | 3 | 100% |
| 2 | Rambachan, A. and N. Shephard (2021) When do common time series estimands have nonparametric causal meaning? | 0.928 | 4 | 4 | 100% |
| 3 | Imbens, G. and D. B. Rubin (2015) Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction | 0.811 | 4 | 2 | 100% |
| 4 | Hernan, M. A. and J. M. Robins (2025) Causal Inference | 0.737 | 3 | 2 | 100% |
| 5 | Lin, Z. and P. Ding (2025) Unifying regression-based and design-based causal inference in time-series experiments | 0.737 | 3 | 2 | 100% |
| 6 | Stock, J. H. and M. W. Watson (2018) Identification and estimation of dynamic causal effects in macroeconomics | 0.737 | 3 | 2 | 100% |
| 7 | Angrist, J. D., Ò. Jordà, and G. M. Kuersteiner (2018) Semiparametric estimates of monetary policy effects: string theory revisited | 0.644 | 2 | 2 | 100% |
| 8 | Angrist, J. D. and G. M. Kuersteiner (2011) Causal effects of monetary shocks: Semiparametric conditional independence tests with a multinomial propensity score | 0.644 | 2 | 2 | 100% |
| 9 | Arkhangelsky, D. and G. Imbens (2024) Causal models for longitudinal and panel data | 0.644 | 2 | 2 | 100% |
| 10 | Ballinari, D. and A. Wehrli (2024) Semiparametric inference for impulse response functions using double/debiased machine learning | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 85 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Calibrated Horizon-Weighted Local Projection Designs for Markov Switchbacks | 0.644 | 2 | 2 |