Policy evaluation during a pandemic

DOI10.1016/j.jeconom.2023.03.009arXiv2105.06927OpenAlexW3160593184MaRDI QIDQ6133349

Tong Li, Brantly Callaway

Publication date: 18 August 2023

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.06927

zbMATH Keywords

policy evaluationunconfoundednessmediatorspandemicdifference-in-differencesCovid-19

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Related Items (1)

Treatment effects in interactive fixed effects models with a small number of time periods

Cites Work

• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Doubly robust difference-in-differences estimators
• Alternative methods for evaluating the impact of interventions. An overview
• Estimating and simulating a SIRD model of COVID-19 for many countries, states, and cities
• Causal impact of masks, policies, behavior on early Covid-19 pandemic in the U.S.
• Estimating the COVID-19 infection rate: anatomy of an inference problem
• Estimating dynamic treatment effects in event studies with heterogeneous treatment effects
• Difference-in-differences with multiple time periods
• Difference-in-differences with variation in treatment timing
• Analysis of the bias of matching and difference-in-difference under alternative earnings and selection processes
• Semiparametric Difference-in-Differences Estimators
• The Estimation of Causal Effects by Difference-in-Difference MethodsEstimation of Spatial Panels
• Dealing with limited overlap in estimation of average treatment effects
• An Introduction to Stochastic Epidemic Models
• Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity
• Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme
• A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS
• Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden
• Quantile treatment effects in difference in differences models with panel data
• Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
• Identification and Inference in Nonlinear Difference-in-Differences Models
• Power and sample size for observational studies of point exposure effects

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