Evaluation of continuous treatment
DOI:
https://doi.org/10.22394/1993-7601-2025-80-93-116Keywords:
обобщенный метод соответствия, функция отклика на непрерывное воздействие, метод разности разностей, оценка политики, несмещенный алгоритм машинного обучения, условный средний эффект воздействияAbstract
Continuous treatment effect evaluation is complicated by confounders. The dose is commonly related to observed characteristics of the treated unit. This paper provides an overview of approaches for evaluating continuous treatment effects. They are 1) the dose-response function; 2) application to panel data based on difference-in-differences; and 3) conditional treatment effect evaluation with debiased machine learning. Empirical examples of the teleworkability effect on unemployment indicators during the COVID‑19 period illustrate the approaches.Agencies
Данная статья является результатом исследовательского проекта, реализованного в рамках Программы фундаментальных исследований Национального исследовательского университета «Высшая школа экономики» (НИУ ВШЭ).Downloads
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Imbens G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 87 (3), 706–710.
Kennedy E. H., Ma Z., McHugh M. D., Small D. S. (2017). Non-parametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society Series B: Statistical Methodology, 79 (4), 1229–1245. DOI: 10.1111/rssb.12212.
King G., Nielsen R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27 (4), 435–454.
Kotyrlo E. (2024). The impact of anti-COVID‑19 restrictions and transitory unemployment insurance policies on unemployment in Russia. Journal of Economic Studies, 51 (1), 111–125. DOI: 10.1108/JES-12-2022-0615.
Mattei A., Bia M. (2009). DoseResponse: Stata module to estimate dose-response function through adjustment for the generalized propensity score. Statistical Software Components S457096. Boston College Department of Economics.
Rosenbaum P. R., Rubin D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
Roth J., Sant’Anna P. H., Bilinski A., Poe J. (2023). What’s trending in difference-in-differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235 (2), 2218–2244. DOI: 10.1016/j.jeconom.2023.03.008.
Semenova V., Chernozhukov V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24 (2), 264–289. DOI: 10.1093/ectj/utaa027.
Shalev-Shwartz S., Ben-David S. (2014). Understanding machine learning: From theory to algorithms. Cambridge University Press.
Sostero M., Milasi S., Hurley J., Fernandez-Macías E., Bisello M. (2020). Teleworkability and the COVID‑19 crisis: A new digital divide? JRC Working Papers Series on Labour, Education and Technology, 2020/05.
Wasserman L. (2006) All of nonparametric statistics. New York: Springer.
Wooldridge J. M. (2023). Simple approaches to nonlinear difference-in-differences with panel data. The Econometrics Journal, 26 (3), C31–C66.
Zhao S., van Dyk D. A., Imai K. (2020). Propensity score-based methods for causal inference in observational studies with non-binary treatments. Statistical Methods in Medical Research, 29 (3), 709–727. DOI: 10.1177/0962280219888745.
