Toru Kitagawa, Weining Wang, Mengshan Xu
arXiv 8 May 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2205.03970 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multi-variate time series. Building on the statistical treatment choice framework, we propose Time-series Empirical Welfare Maximization (T-EWM) methods to estimate an optimal policy rule by maximizing an empirical welfare criterion constructed using nonparametric potential outcome time series. We characterize conditions under which T-EWM consistently learns a policy choice that is optimal in terms of conditional welfare given the time-series history. We derive a nonasymptotic upper bound for conditional welfare regret. To illustrate the implementation and uses of T-EWM, we perform simulation studies and apply the method to estimate optimal restriction rules against Covid-19.
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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 | I. Bojinov and N. Shephard (2019) Time series experiments and causal estimands: exact randomization tests and trading | 1.000 | 14 | 3 | 100% |
| 2 | T. Kitagawa and A. Tetenov (2018) Who should be treated? Empirical welfare maximization methods for treatment choice | 0.920 | 9 | 5 | 78% |
| 3 | C. F. Manski (2004) Statistical treatment rules for heterogeneous populations | 0.874 | 8 | 2 | 100% |
| 4 | S. Athey and S. Wager (2021) Policy learning with observational data | 0.843 | 3 | 3 | 100% |
| 5 | T. Kitagawa, S. Sakaguchi, and A. Tetenov (2021) Constrained classification and policy learning | 0.737 | 3 | 2 | 100% |
| 6 | J. D. Angrist, Ò. Jordà, and G. M. Kuersteiner (2018) Semiparametric estimates of monetary policy effects: string theory revisited | 0.737 | 3 | 2 | 100% |
| 7 | A. Rambachan and N. Shepherd (2021) When do common time series estimands have nonparametric causal meaning? | 0.737 | 3 | 2 | 100% |
| 8 | L. Kallenberg (2016) Markov decision processes | 0.644 | 3 | 2 | 67% |
| 9 | E. Mbakop and M. Tabord-Meehan Model selection for treatment choice: Penalized welfare maximization | 0.644 | 2 | 2 | 100% |
| 10 | S. Sakaguchi (2021) Estimation of optimal dynamic treatment assignment rules under policy constraint | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 68 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.