Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning

• Tác giả: Michel F. C Haddad, Martin Huber, Lucas Z Zhang
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.31 Machine learning
• Thông tin xuất bản: 2024
• Bộ sưu tập: Metadata
• ID: 204658

Tóm tắt

We propose a difference-in-differences (DiD) method for a time-varying continuous treatment and multiple time periods. Our framework assesses the average treatment effect on the treated (ATET) when comparing two non-zero treatment doses. The identification is based on a conditional parallel trend assumption imposed on the mean potential outcome under the lower dose, given observed covariates and past treatment histories. We employ kernel-based ATET estimators for repeated cross-sections and panel data adopting the double/debiased machine learning framework to control for covariates and past treatment histories in a data-adaptive manner. We also demonstrate the asymptotic normality of our estimation approach under specific regularity conditions. In a simulation study, we find a compelling finite sample performance of undersmoothed versions of our estimators in setups with several thousand observations.