Dynamic synthetic control method for evaluating treatment effects in auto-regressive processes
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2024 · Volume 86 · Issue 1
Abstract
Motivated by evaluating the effects of air pollution alerts on air quality, we propose the dynamic synthetic control method for micro-level data with time-varying confounders and spatial dependence under an auto-regressive model setting. We employ the empirical likelihood to define the synthetic control weights, which ensures a unique solution and permits theoretical analysis. The dynamic matching increases the feasibility of matching and enables us to assess the unconfoundedness assumption using pre-treatment data. For statistical inference, we develop a normalised placebo test to address the asymmetry issue. The method is illustrated and evaluated on numerical simulations and a case study on air pollution alerts.
Keywords
empirical likelihood, policy intervention, spatial dependence, time-varying confounding, treatment effect
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