Forests for Differences: Robust Causal Inference Beyond Parametric DiD

Hugo Gobato Souto; Francisco Louzada Neto

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arXiv · 2505.09706

Forests for Differences: Robust Causal Inference Beyond Parametric DiD

Hugo Gobato Souto·Francisco Louzada Neto

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Abstract

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.

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Hugo Gobato Souto, Francisco Louzada Neto. 2025-05-14. Forests for Differences: Robust Causal Inference Beyond Parametric DiD. https://arxiv.org/abs/2505.09706

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