Aditya Ghosh, Stefan Wager
arXiv 17 Dec 2025 · Statistics — Methodology
arXiv:2512.15244 · PDF · Extracted main text
Consider a setting where we regularly monitor patients' fasting blood sugar, and declare them to have prediabetes (and encourage preventative care) if this number crosses a pre-specified threshold. The sharp, threshold-based treatment policy suggests that we should be able to estimate the long-term benefit of this preventative care by comparing the health trajectories of patients with blood sugar measurements right above and below the threshold. A naive regression-discontinuity analysis, however, is not applicable here, as it ignores the temporal dynamics of the problem where, e.g., a patient just below the threshold on one visit may become prediabetic (and receive treatment) following their next visit. Here, we study thresholding designs in general dynamic systems, and show that simple reduced-form characterizations remain available for a relevant causal target, namely a dynamic marginal policy effect at the treatment threshold. We develop a local-linear-regression approach for estimation and inference of this estimand, and demonstrate promise of our approach in numerical experiments.
appendix boundary found by appendix_command · 32% of the source is main text. Read the extracted text to check this.
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 | Hahn, J., P. Todd, and W. V. der Klaauw (2001) Identification and estimation of treatment effects with a regression-discontinuity design | 0.874 | 6 | 2 | 100% |
| 2 | Imbens, G. and T. Lemieux (2008) Regression discontinuity designs: A guide to practice | 0.874 | 5 | 2 | 100% |
| 3 | Imbens, G. and K. Kalyanaraman (2012) Optimal bandwidth choice for the regression discontinuity estimator | 0.811 | 4 | 2 | 100% |
| 4 | Sutton, R. S., D. McAllester, S. Singh, and Y. Mansour (1999) Policy gradient methods for reinforcement learning with function approximation | 0.737 | 3 | 2 | 100% |
| 5 | Iizuka, T., K. Nishiyama, B. Chen, and K. Eggleston (2021) False alarm? estimating the marginal value of health signals | 0.693 | 5 | 1 | 100% |
| 6 | Hsu, Y.-C. and S. Shen (2024) Dynamic regression discontinuity under treatment effect heterogeneity | 0.693 | 5 | 1 | 100% |
| 7 | Robins, J (1986) A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy w… | 0.693 | 5 | 1 | 100% |
| 8 | Sutton, R. S. and A. G. Barto (2018) Reinforcement learning: an introduction\/ (Second ed.) | 0.585 | 3 | 1 | 100% |
| 9 | Armstrong, T. B. and M. Kolesár (2018) Optimal inference in a class of regression models | 0.511 | 2 | 1 | 100% |
| 10 | Calonico, S., M. D. Cattaneo, and R. Titiunik (2014) Robust nonparametric confidence intervals for regression-discontinuity designs | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | What is the Long-Term Value of Reliability? | 0.644 | 2 | 2 |