# DRCFS: Doubly Robust Causal Feature Selection

arXiv: https://arxiv.org/abs/2306.07024 · HTML: https://mlsanity.com/paper/2306.07024/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 63 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [Causal Discovery with Continuous Additive Noise Models](https://mlsanity.com/paper/1309.6779/) (2013)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [Foundations of Structural Causal Models with Cycles and Latent Variables](https://mlsanity.com/paper/1611.06221/) (2016)
- [Exact post-selection inference, with application to the lasso](https://mlsanity.com/paper/1311.6238/) (2013)
- [Causal Structure Learning](https://mlsanity.com/paper/1706.09141/) (2017)
- [Learning Sparse Causal Models is not NP-hard](https://mlsanity.com/paper/1309.6824/) (2013)
- [Exact Post-Selection Inference for Sequential Regression Procedures](https://mlsanity.com/paper/1401.3889/) (2014)
- [High-dimensional consistency in score-based and hybrid structure learning](https://mlsanity.com/paper/1507.02608/) (2015)
- [RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests](https://mlsanity.com/paper/2110.03031/) (2021)
- [De-biasing the Lasso: Optimal Sample Size for Gaussian Designs](https://mlsanity.com/paper/1508.02757/) (2015)
- [A Unified View of Causal and Non-causal Feature Selection](https://mlsanity.com/paper/1802.05844/) (2018)
- [GeneDisco: A Benchmark for Experimental Design in Drug Discovery](https://mlsanity.com/paper/2110.11875/) (2021)
- [A significance test for forward stepwise model selection](https://mlsanity.com/paper/1405.3920/) (2014)
- [Causal Feature Selection via Orthogonal Search](https://mlsanity.com/paper/2007.02938/) (2020)
- [Applications of statistical causal inference in software engineering](https://mlsanity.com/paper/2211.11482/) (2022)
- [What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems](https://mlsanity.com/paper/1904.02826/) (2019)
- [Evaluating Digital Tools for Sustainable Agriculture using Causal Inference](https://mlsanity.com/paper/2211.03195/) (2022)

## Built on by (most cited first, 12 of 12)

- [Doubly Robust Alignment for Large Language Models](https://mlsanity.com/paper/2506.01183/) (2025)
- [Learning Decision Policies with Instrumental Variables through Double Machine Learning](https://mlsanity.com/paper/2405.08498/) (2024)
- [Double Machine Learning Based Structure Identification from Temporal Data](https://mlsanity.com/paper/2311.06012/) (2023)
- [Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2409.04473/) (2024)
- [Statistical Inference in Reinforcement Learning: A Selective Survey](https://mlsanity.com/paper/2502.16195/) (2025)
- [AI Alignment in Medical Imaging: Unveiling Hidden Biases Through Counterfactual Analysis](https://mlsanity.com/paper/2504.19621/) (2025)
- [Causally-Aware Unsupervised Feature Selection Learning](https://mlsanity.com/paper/2410.12224/) (2024)
- ["Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift](https://mlsanity.com/paper/2506.00756/) (2025)
- [IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models](https://mlsanity.com/paper/2404.13504/) (2024)
- [Causal Bayesian Optimization with Unknown Graphs](https://mlsanity.com/paper/2503.19554/) (2025)
- [Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning](https://mlsanity.com/paper/2509.13763/) (2025)
- [Detecting Changes in Causal Dependence with Kernels and Copulas](https://mlsanity.com/paper/2605.05809/) (2026)

## 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.