mlsanityData to 7 Oct 2026

Causal Effect Inference for Structured Treatments

arXiv 2106.01939 ↗First posted 3 Jun 2021cs.LG stat.MLcited by 38

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Abstract (arXiv)

We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e.g., graphs, images, texts). Given a weak condition on the effect, we propose the generalized Robinson decomposition, which (i) isolates the causal estimand (reducing regularization bias), (ii) allows one to plug in arbitrary models for learning, and (iii) possesses a quasi-oracle convergence guarantee under mild assumptions. In experiments with small-world and molecular graphs we demonstrate that our approach outperforms prior work in CATE estimation.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 41 of its 67 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

41 of 67 references.

References · 41

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  5. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  6. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  7. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  8. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  9. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  10. 2016Learning Representations for Counterfactual Inferencecited by 327
  11. 2016Generalized Random Forestscited by 309
  12. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
29 more
  1. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  2. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  3. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  4. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  5. 2019Kernel Instrumental Variable Regressioncited by 118
  6. 2020An Introduction to Proximal Causal Learningcited by 91
  7. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  8. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  9. 2019The HSIC Bottleneck: Deep Learning without Back-Propagationcited by 77
  10. 2020Learning Deep Features in Instrumental Variable Regressioncited by 65
  11. 2018Offline Multi-Action Policy Learning: Generalization and Optimizationcited by 64
  12. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  13. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  14. 2019Estimating Treatment Effects with Causal Forests: An Applicationcited by 58
  15. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  16. 2018Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networkscited by 55
  17. 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 54
  18. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  19. 2020RealCause: Realistic Causal Inference Benchmarkingcited by 44
  20. 2018DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingcited by 42
  21. 2017Estimation of causal effects with multiple treatments: a review and new ideascited by 29
  22. 2020Invariant Representation Learning for Treatment Effect Estimationcited by 25
  23. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  24. 2020Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Reviewcited by 20
  25. 2020GraphITE: Estimating Individual Effects of Graph-structured Treatmentscited by 18
  26. 2019Permutation Weightingcited by 14
  27. 2020Set Prediction without Imposing Structure as Conditional Density Estimationcited by 12
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  29. 2020Causal Inference for Spatial Treatmentscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 7; 2024: 8; 2025: 6; 2026: 4.

38 papers.

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  1. 2022When Do Flat Minima Optimizers Work?cited by 78
  2. 2023Spawrious: A Benchmark for Fine Control of Spurious Correlation Biasescited by 41
  3. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  4. 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25
  5. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  6. 2022Image-based Treatment Effect Heterogeneitycited by 20
  7. 2023Zero-shot causal learningcited by 17
  8. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  9. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  10. 2023Causal Effect Estimation: Recent Advances, Challenges, and Opportunitiescited by 14
  11. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  12. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
26 more
  1. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  2. 2023Integrating Earth Observation Data into Causal Inference: Challenges and Opportunitiescited by 8
  3. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  4. 2024Contrastive representations of high-dimensional, structured treatmentscited by 7
  5. 2022Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimationcited by 5
  6. 2024Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptationcited by 5
  7. 2022Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africacited by 4
  8. 2025Agentic Personalisation of Cross-Channel Marketing Experiencescited by 3
  9. 2022Scalar is Not Enough: Vectorization-based Unbiased Learning to Rankcited by 2
  10. 2024Structured Learning of Compositional Sequential Interventionscited by 2
  11. 2024DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classificationcited by 2
  12. 2025RIDE: Enhancing Large Language Model Alignment through Restyled In-Context Learning Demonstration Exemplarscited by 2
  13. 2025KANITE: Kolmogorov-Arnold Networks for ITE estimationcited by 2
  14. 2026Causal methods for LLM development and evaluationcited by 2
  15. 2022Interpretable Deep Causal Learning for Moderation Effectscited by 1
  16. 2023TraffNet: Learning Causality of Traffic Generation for What-if Predictioncited by 1
  17. 2024I See, Therefore I Do: Estimating Causal Effects for Image Treatmentscited by 1
  18. 2025Doubly Robust Fusion of Many Treatments for Policy Learningcited by 1
  19. 2025Leveraging semantic similarity for experimentation with AI-generated treatmentscited by 1
  20. 2024Compositional Models for Estimating Causal Effectscited by 0
  21. 2024Estimating the Causal Effects of T Cell Receptorscited by 0
  22. 2024Treatment Effect Estimation for Graph-Structured Targetscited by 0
  23. 2025Conditional Front-door Adjustment for Heterogeneous Treatment Assignment Effect Estimation Under Non-adherencecited by 0
  24. 2026Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatmentscited by 0
  25. 2026Treatment Effect Estimation with Differentiated Networked Effect on Graph Datacited by 0
  26. 2026Causal Representation Learning for Generalisable Recommendationcited by 0