Ruei-Chi Lee
arXiv 8 Sep 2026 · Econometrics
arXiv:2609.10617 · PDF · Extracted main text
Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this paper, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear effects of observed and unobserved covariates, we propose a nonparametric model for untreated outcome, interpreted as a time-varying factor model via sieve approximation. Estimating the time-varying factor model is challenging due to the boundary bias and identification. Our estimation method based on diversified projection can effectively address these issues. We develop an asymptotic distribution theory to facilitate inference for the ATEL estimator. In an empirical application, we apply our proposed methodology to assess the impact of right-to-carry laws on violent crime rate.
appendix boundary found by none_found · 100% 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 | Liangjun Su and Xia Wang (2017) On time-varying factor models: Estimation and testing | 1.000 | 9 | 5 | 100% |
| 2 | Alberto Abadie and Alexis Diamond and Jens Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program | 1.000 | 5 | 3 | 100% |
| 3 | Jianqing Fan and Yuan Liao (2022) Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors | 0.874 | 5 | 2 | 100% |
| 4 | Kathleen T. Li and Garrett P. Sonnier (2023) Statistical Inference for the Factor Model Approach to Estimate Causal Effects in Quasi-Experimental Settings | 0.843 | 3 | 3 | 100% |
| 5 | Gobillon, Laurent and Magnac, Thierry (2016) Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls | 0.737 | 3 | 2 | 100% |
| 6 | Jushan Bai and Serena Ng (2021) Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data | 0.737 | 3 | 2 | 100% |
| 7 | Fernández-Val, Iván and Freeman, Hugo and Weidner, Martin (2021) Low-rank approximations of nonseparable panel models | 0.644 | 2 | 2 | 100% |
| 8 | Abadie, Alberto and Gardeazabal, Javier (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.644 | 2 | 2 | 100% |
| 9 | Jushan Bai (2009) Panel Data Models with Interactive Fixed Effects | 0.644 | 2 | 2 | 100% |
| 10 | Aneja, Abhay and Donohue, John and Zhang, Alexandria (2011) The Impact of Right-to-Carry Laws and the NRC Report: Lessons for the Empirical Evaluation of Law and Policy | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 53 scored citations.