mlsanityData to 7 Oct 2026

Factor Informed Double Deep Learning For Average Treatment Effect Estimation

arXiv 2508.17136 ↗First posted 23 Aug 2025stat.ML cs.LG math.STcited by 2

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Abstract (arXiv)

We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have sparse nonlinear effects on the propensity and outcome models. We present the use of a Double Deep Learning strategy for estimation, which involves combining recently developed factor-augmented deep learning-based estimators, FAST-NN, for both the response functions and propensity scores to achieve our goal. By using FAST-NN, our method can select variables that contribute to propensity and outcome models in a completely nonparametric and algorithmic manner and adaptively learn low-dimensional function structures through neural networks. Our proposed novel estimator, FIDDLE (Factor Informed Double Deep Learning Estimator), estimates ATE based on the framework of augmented inverse propensity weighting AIPW with the FAST-NN-based response and propensity estimates. FIDDLE consistently estimates ATE even under model misspecification and is flexible to also allow for low-dimensional covariates. Our method achieves semiparametric efficiency under a very flexible family of propensity and outcome models. We present extensive numerical studies on synthetic and real datasets to support our theoretical guarantees and establish the advantages of our methods over other traditional choices, especially when the data dimension is large.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 54 references that are in the map. First posted on arXiv 23 Aug 2025.

Builds on

18 of 54 references.

References · 18

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2016Error bounds for approximations with deep ReLU networkscited by 567
  3. 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
  4. 2016Generalized Random Forestscited by 309
  5. 2020Spectral Methods for Data Science: A Statistical Perspectivecited by 111
  6. 2015A Deep Learning Approach to Structured Signal Recoverycited by 43
  7. 2013Robust Inference on Average Treatment Effects with Possibly More Covariates than Observationscited by 38
  8. 2022Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regressioncited by 31
  9. 2022How do noise tails impact on deep ReLU networks?cited by 25
  10. 2023Deep Neural Networks for Nonparametric Interaction Models with Diverging Dimensioncited by 15
  11. 2018Robust high dimensional factor models with applications to statistical machine learningcited by 13
  12. 2016Factor-Adjusted Regularized Model Selectioncited by 9
6 more
  1. 2019Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factorscited by 8
  2. 2024Factor Augmented Tensor-on-Tensor Neural Networkscited by 7
  3. 2021Optimal Covariate Balancing Conditions in Propensity Score Estimationcited by 6
  4. 2025Multilook Coherent Imaging: Theoretical Guarantees and Algorithmscited by 4
  5. 2021Dimension-Free Average Treatment Effect Inference with Deep Neural Networkscited by 3
  6. 2024Factor Adjusted Spectral Clustering for Mixture Modelscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysiscited by 0
  2. 2026Minimax Theory of Likelihood-Based Deep Learning for Speckle Regressioncited by 0