mlsanityData to 7 Oct 2026

DRCFS: Doubly Robust Causal Feature Selection

arXiv 2306.07024 ↗First posted 12 Jun 2023cs.LG stat.MEcited by 12

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Abstract (arXiv)

Knowing the features of a complex system that are highly relevant to a particular target variable is of fundamental interest in many areas of science. Existing approaches are often limited to linear settings, sometimes lack guarantees, and in most cases, do not scale to the problem at hand, in particular to images. We propose DRCFS, a doubly robust feature selection method for identifying the causal features even in nonlinear and high dimensional settings. We provide theoretical guarantees, illustrate necessary conditions for our assumptions, and perform extensive experiments across a wide range of simulated and semi-synthetic datasets. DRCFS significantly outperforms existing state-of-the-art methods, selecting robust features even in challenging highly non-linear and high-dimensional problems.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 63 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

20 of 63 references.

References · 20

  1. 2016Generalized Random Forestscited by 309
  2. 2013Direct and Indirect Effectscited by 276
  3. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  4. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
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  8. 2017Causal Structure Learningcited by 84
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  10. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 47
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  12. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
8 more
  1. 2015De-biasing the Lasso: Optimal Sample Size for Gaussian Designscited by 35
  2. 2018A Unified View of Causal and Non-causal Feature Selectioncited by 23
  3. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  4. 2014A significance test for forward stepwise model selectioncited by 17
  5. 2022Applications of statistical causal inference in software engineeringcited by 12
  6. 2019What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problemscited by 11
  7. 2020Causal Feature Selection via Orthogonal Searchcited by 11
  8. 2022Evaluating Digital Tools for Sustainable Agriculture using Causal Inferencecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 6; 2026: 1.

12 papers.

  1. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  2. 2024Learning Decision Policies with Instrumental Variables through Double Machine Learningcited by 5
  3. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  4. 2024Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysiscited by 2
  5. 2025Statistical Inference in Reinforcement Learning: A Selective Surveycited by 2
  6. 2025AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysiscited by 1
  7. 2024IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Modelscited by 0
  8. 2024Causally-Aware Unsupervised Feature Selection Learningcited by 0
  9. 2025Causal Bayesian Optimization with Unknown Graphscited by 0
  10. 2025"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Driftcited by 0
  11. 2025Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learningcited by 0
  12. 2026Detecting Changes in Causal Dependence with Kernels and Copulascited by 0