mlsanityData to 7 Oct 2026

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

arXiv 1902.00981 ↗First posted 3 Feb 2019cs.LG stat.MLcited by 90

Not on the map. Why?

Abstract (arXiv)

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.

Key facts. Cited by 90 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Feb 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 23; 2023: 16; 2024: 24; 2025: 9; 2026: 9.

90 papers.

Sort:

  1. 2024Causal machine learning for predicting treatment outcomescited by 73
  2. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  3. 2021Causal Effect Inference for Structured Treatmentscited by 38
  4. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  5. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  6. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  7. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  8. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  9. 2022Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventionscited by 20
  10. 2024Conformal Prediction for Causal Effects of Continuous Treatmentscited by 19
  11. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  12. 2022DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimationcited by 16
38 more
  1. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  2. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  3. 2021NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatmentscited by 14
  4. 2023Causal Effect Estimation: Recent Advances, Challenges, and Opportunitiescited by 14
  5. 2022Adversarial Counterfactual Environment Model Learningcited by 12
  6. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  7. 2021Matching Learned Causal Effects of Neural Networks with Domain Priorscited by 10
  8. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
  9. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  10. 2022Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounderscited by 10
  11. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  12. 2023Adversarially Balanced Representation for Continuous Treatment Effect Estimationcited by 10
  13. 2022Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Datacited by 9
  14. 2022Generalization bounds and algorithms for estimating conditional average treatment effect of dosagecited by 9
  15. 2021CETransformer: Casual Effect Estimation via Transformer Based Representation Learningcited by 8
  16. 2022A Neural Mean Embedding Approach for Back-door and Front-door Adjustmentcited by 8
  17. 2024Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothingcited by 7
  18. 2024Proximity Matters: Local Proximity Enhanced Balancing for Treatment Effect Estimationcited by 7
  19. 2022Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trialscited by 6
  20. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6
  21. 2024Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimationcited by 6
  22. 2022Learning Instrumental Variable from Data Fusion for Treatment Effect Estimationcited by 5
  23. 2024Learning Decision Policies with Instrumental Variables through Double Machine Learningcited by 5
  24. 2024Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growthcited by 5
  25. 2024CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Eventscited by 5
  26. 2021Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxiescited by 4
  27. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  28. 2023Estimation of individual causal effects in network setup for multiple treatmentscited by 4
  29. 2024Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weightscited by 4
  30. 2024Uplift modeling with continuous treatments: A predict-then-optimize approachcited by 4
  31. 2025In-silico biological discovery with large perturbation modelscited by 4
  32. 2026Causal Foundation Models with Continuous Treatmentscited by 4
  33. 2023SpaCE: The Spatial Confounding Environmentcited by 3
  34. 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Datacited by 3
  35. 2024PairNet: Training with Observed Pairs to Estimate Individual Treatment Effectcited by 3
  36. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  37. 2022Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effectscited by 2
  38. 2023Towards Learning and Explaining Indirect Causal Effects in Neural Networkscited by 2

50 most cited of 90 All 90 (JSON)