Causal inference using factor models

Bai, Jushan and Wang, Peng (2024): Causal inference using factor models.

Abstract

We propose a framework for causal inference using factor models. We base our identification strategy on the assumption that policy interventions cause structural breaks in the factor loadings for the treated units. The method allows heterogeneous trends and is easy to implement. We compare our method with the synthetic control methods of Abadie, et al (2010, 2015), and obtain similar results. Additionally, we provide confidence intervals for the causal effects. Our approach expands the toolset for causal inference.

• Item Type: MPRA Paper
• Language: English
• Keywords: synthetic control, difference-in-differences, structural breaks, latent factors.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation

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