mlsanityData to 7 Oct 2026

Forests for Differences: Robust Causal Inference Beyond Parametric DiD

arXiv 2505.09706 ↗First posted 14 May 2025stat.ME cs.LG stat.MLcited by 0

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Abstract (arXiv)

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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References · 20

  1. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  2. 2016Generalized Random Forestscited by 309
  3. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  4. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
  5. 2016Generalized Optimal Matching Methods for Causal Inferencecited by 39
  6. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Pythoncited by 23
  7. 2018Design-based Analysis in Difference-In-Differences Settings with Staggered Adoptioncited by 15
  8. 2020Stochastic tree ensembles for regularized nonlinear regressioncited by 14
  9. 2016Regularization and confounding in linear regression for treatment effect estimationcited by 12
  10. 2023Prediction under interventions: evaluation of counterfactual performance using longitudinal observational datacited by 8
  11. 2022Stochastic Tree Ensembles for Estimating Heterogeneous Effectscited by 7
  12. 2021Regression Discontinuity Designscited by 5
8 more
  1. 2023Estimating and evaluating counterfactual prediction modelscited by 3
  2. 2023Universal Difference-in-Differences for Causal Inference in Epidemiologycited by 2
  3. 2023Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-makingcited by 2
  4. 2024Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approachcited by 2
  5. 2020Connecting Instrumental Variable methods for causal inference to the Estimand Frameworkcited by 1
  6. 2022Nonparametric Estimation via Partial Derivativescited by 1
  7. 2024Modified BART for Learning Heterogeneous Effects in Regression Discontinuity Designscited by 1
  8. 2024Ablation Studies for Novel Treatment Effect Estimation Modelscited by 1

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