mlsanityData to 7 Oct 2026

Doubly Robust Counterfactual Classification

arXiv 2301.06199 ↗First posted 15 Jan 2023cs.LG stat.ME stat.MLcited by 5

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Abstract (arXiv)

We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a general counterfactual classifier, where we can incorporate flexible constraints by casting the classification problem as a nonlinear mathematical program involving counterfactuals. We go on to analyze the rates of convergence of the estimator and provide a closed-form expression for its asymptotic distribution. Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast √n rates with tractable inference even when using nonparametric machine learning approaches. We study the empirical performance of our methods by simulation and apply them for recidivism risk prediction.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 18 of its 61 references that are in the map. First posted on arXiv 15 Jan 2023.

Builds on

18 of 61 references.

References · 18

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  4. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  5. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  6. 2022Semiparametric doubly robust targeted double machine learning: a reviewcited by 129
  7. 2017Double/Debiased/Neyman Machine Learning of Treatment Effectscited by 76
  8. 2020Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Oddscited by 40
  9. 2020Counterfactual Predictions under Runtime Confoundingcited by 22
  10. 2017Estimating Individual Treatment Effect in Observational Data Using Random Forest Methodscited by 21
  11. 2016Algorithms for Fitting the Constrained Lassocited by 17
  12. 2015Structural Nested Models and G-estimation: The Partially Realized Promisecited by 15
6 more
  1. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
  2. 2017Semiparametric theorycited by 7
  3. 2020Prediction meets causal inference: the role of treatment in clinical prediction modelscited by 7
  4. 2018Generalized Linear Models with Linear Constraints for Microbiome Compositional Datacited by 4
  5. 2020Hierarchical Marketing Mix Models with Sign Constraintscited by 4
  6. 2022Counterfactual Mean-variance Optimizationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 2.

5 papers.

  1. 2023Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learningcited by 6
  2. 2025Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shiftcited by 1
  3. 2026Omitted Variable Bias in Language Models Under Distribution Shiftcited by 1
  4. 2026Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approachcited by 1
  5. 2023Mind the Gap: Optimal and Equitable Encouragement Policiescited by 0