用非参数方法提升因果推断精度，解决政策评估中的异质效应问题
Forests for Differences: Robust Causal Inference Beyond Parametric DiD
- 基于平行趋势假设重构模型，增强复杂面板数据下的估计稳定性
- 在非线性、选择偏差等挑战下优于传统基准方法，尤其在异质效应场景表现突出
- 适用于政策评估等现实场景，可揭示人口规模等条件下的差异效应
本文提出差异-差异贝叶斯因果森林（DiD-BCF），一种新型非参数模型，解决滞后实施和异质处理效应等关键挑战。该模型统一支持平均处理效应（ATE）、组平均效应（GATE）及条件平均效应（CATE）的估计。核心创新在于基于平行趋势假设（PTA）的重参数化，显著提升复杂面板数据中的估计精度与稳定性。大量模拟实验表明，DiD-BCF在非线性、选择偏差及效应异质性条件下均优于现有基准方法。应用于美国最低工资政策研究，模型揭示了县级人口规模相关的显著条件效应异质性，这是传统方法难以捕捉的。该模型为现代差分-差分应用提供了稳健且灵活的因果推断工具。
原文摘要 · Abstract (English)
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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