mlsanityData to 7 Oct 2026

Causal Inference from Small High-dimensional Datasets

arXiv 2205.09281 ↗First posted 19 May 2022cs.LG stat.MLcited by 3

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Abstract (arXiv)

Many methods have been proposed to estimate treatment effects with observational data. Often, the choice of the method considers the application's characteristics, such as type of treatment and outcome, confounding effect, and the complexity of the data. These methods implicitly assume that the sample size is large enough to train such models, especially the neural network-based estimators. What if this is not the case? In this work, we propose Causal-Batle, a methodology to estimate treatment effects in small high-dimensional datasets in the presence of another high-dimensional dataset in the same feature space. We adopt an approach that brings transfer learning techniques into causal inference. Our experiments show that such an approach helps to bring stability to neural network-based methods and improve the treatment effect estimates in small high-dimensional datasets.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

18 of 32 references.

References · 18

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  4. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  5. 2018The Blessings of Multiple Causescited by 103
  6. 2020An Introduction to Proximal Causal Learningcited by 91
  7. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  8. 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 54
  9. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  10. 2018Combining multiple observational data sources to estimate causal effectscited by 29
  11. 2021A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Modelscited by 29
  12. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
6 more
  1. 2021BayesIMP: Uncertainty Quantification for Causal Data Fusioncited by 21
  2. 2021Multi-Source Causal Inference Using Control Variatescited by 13
  3. 2022A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settingscited by 8
  4. 2021Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confoundingcited by 7
  5. 2020ParKCa: Causal Inference with Partially Known Causescited by 4
  6. 2020Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimationcited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1.

3 papers.

  1. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  2. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  3. 2022BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effectcited by 1