mlsanityData to 7 Oct 2026

Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects

arXiv 2205.14714 ↗First posted 29 May 2022stat.ML cs.LG stat.APcited by 10

Not on the map. Why?

Abstract (arXiv)

Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data. In addition to Machine Learning based-models, nonparametric estimators called meta-learners have been developed to estimate the CATE with the main advantage of not restraining the estimation to a specific supervised learning method. This task becomes, however, more complicated when the treatment is not binary as some limitations of the naive extensions emerge. This paper looks into meta-learners for estimating the heterogeneous effects of multi-valued treatments. We consider different meta-learners, and we carry out a theoretical analysis of their error upper bounds as functions of important parameters such as the number of treatment levels, showing that the naive extensions do not always provide satisfactory results. We introduce and discuss meta-learners that perform well as the number of treatments increases. We empirically confirm the strengths and weaknesses of those methods with synthetic and semi-synthetic datasets.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 25 of its 66 references that are in the map. First posted on arXiv 29 May 2022.

Builds on

25 of 66 references.

References · 25

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  4. 2016Learning Representations for Counterfactual Inferencecited by 327
  5. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  6. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  7. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 239
  8. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  9. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  10. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  11. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  12. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
13 more
  1. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  2. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  3. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  4. 2017Some methods for heterogeneous treatment effect estimation in high-dimensionscited by 48
  5. 2021Causal Effect Inference for Structured Treatmentscited by 38
  6. 2016Recursive Partitioning for Personalization using Observational Datacited by 28
  7. 2022On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challengescited by 28
  8. 2019Uplift Modeling for Multiple Treatments with Cost Optimizationcited by 25
  9. 2020Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Reviewcited by 20
  10. 2020GraphITE: Estimating Individual Effects of Graph-structured Treatmentscited by 18
  11. 2020Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcomecited by 13
  12. 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
  13. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4

Built on by

Cited by year (2026 to 7 Oct)

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

10 papers.

  1. 2024Model-agnostic meta-learners for estimating heterogeneous treatment effects over timecited by 18
  2. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  3. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  4. 2023Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learningcited by 4
  5. 2025Hybrid Meta-learners for Estimating Heterogeneous Treatment Effectscited by 4
  6. 2025Flow-based Generative Modeling of Potential Outcomes and Counterfactualscited by 1
  7. 2026GUSH3R: Everyone Everywhere All at Once as Gaussianscited by 1
  8. 2024Robust CATE Estimation Using Novel Ensemble Methodscited by 0
  9. 2024Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatmentscited by 0
  10. 2025SCOPE: Sequential Causal Optimization of Process Interventionscited by 0