mlsanityData to 14 Oct 2026

Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation

arXiv 2403.14232 ↗First posted 21 Mar 2024cs.LGcited by 6

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Abstract (arXiv)

Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 25 of its 51 references that are in the map. First posted on arXiv 21 Mar 2024.

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25 of 51 references.

References · 25

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13 more
  1. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  2. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  3. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 64
  4. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 61
  5. 2021CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendationcited by 46
  6. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
  7. 2018DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingcited by 42
  8. 2013Partial Distance Correlation with Methods for Dissimilaritiescited by 20
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  10. 2022Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregationcited by 14
  11. 2022Contrastive Learning with Positive-Negative Frame Mask for Music Representationcited by 10
  12. 2020Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposurescited by 8
  13. 2022Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Predictioncited by 8

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Cited by year

Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024Deep Autoregressive Models as Causal Inference Enginescited by 6
  2. 2024MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlationcited by 5
  3. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  4. 2024Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimationcited by 2
  5. 2025A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confoundingcited by 0
  6. 2025A Computational Theory for Efficient Mini Agent Evaluation with Causal Guaranteescited by 0