Forests for Differences: Robust Causal Inference Beyond Parametric DiD
It provides a robust tool for researchers and policymakers needing nuanced causal inference in DiD applications, though it is incremental as it builds on existing DiD and causal forest methods.
The paper tackles challenges in Difference-in-Differences (DiD) estimation, such as staggered adoption and heterogeneous effects, by introducing DiD-BCF, a non-parametric model that outperforms benchmarks in simulations and reveals nuanced insights in a U.S. minimum wage policy application.
This paper introduces the Difference-in-Differences Bayesian Causal Forest (DiD-BCF), a novel non-parametric model addressing key challenges in DiD estimation, such as staggered adoption and heterogeneous treatment effects. DiD-BCF provides a unified framework for estimating Average (ATE), Group-Average (GATE), and Conditional Average Treatment Effects (CATE). A core innovation, its Parallel Trends Assumption (PTA)-based reparameterization, enhances estimation accuracy and stability in complex panel data settings. Extensive simulations demonstrate DiD-BCF's superior performance over established benchmarks, particularly under non-linearity, selection biases, and effect heterogeneity. Applied to U.S. minimum wage policy, the model uncovers significant conditional treatment effect heterogeneity related to county population, insights obscured by traditional methods. DiD-BCF offers a robust and versatile tool for more nuanced causal inference in modern DiD applications.