mlsanityData to 7 Oct 2026

On Inductive Biases for Heterogeneous Treatment Effect Estimation

arXiv 2106.03765 ↗First posted 7 Jun 2021stat.ML cs.LGcited by 74

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Abstract (arXiv)

We investigate how to exploit structural similarities of an individual's potential outcomes (POs) under different treatments to obtain better estimates of conditional average treatment effects in finite samples. Especially when it is unknown whether a treatment has an effect at all, it is natural to hypothesize that the POs are similar - yet, some existing strategies for treatment effect estimation employ regularization schemes that implicitly encourage heterogeneity even when it does not exist and fail to fully make use of shared structure. In this paper, we investigate and compare three end-to-end learning strategies to overcome this problem - based on regularization, reparametrization and a flexible multi-task architecture - each encoding inductive bias favoring shared behavior across POs. To build understanding of their relative strengths, we implement all strategies using neural networks and conduct a wide range of semi-synthetic experiments. We observe that all three approaches can lead to substantial improvements upon numerous baselines and gain insight into performance differences across various experimental settings.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). Builds on 22 of its 73 references that are in the map. First posted on arXiv 7 Jun 2021.

Builds on

22 of 73 references.

References · 22

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  3. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  4. 2016Cross-stitch Networks for Multi-task Learningcited by 466
  5. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  6. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  7. 2016Learning Representations for Counterfactual Inferencecited by 327
  8. 2016Domain Separation Networkscited by 317
  9. 2016Generalized Random Forestscited by 309
  10. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  11. 2018Explicit Inductive Bias for Transfer Learning with Convolutional Networkscited by 226
  12. 2015The Benefit of Multitask Representation Learningcited by 194
10 more
  1. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  2. 2019Orthogonal Statistical Learningcited by 150
  3. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  4. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  5. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
  6. 2018Learning Weighted Representations for Generalization Across Designscited by 41
  7. 2017Deep Counterfactual Networks with Propensity-Dropoutcited by 30
  8. 2016Hypothesis Transfer Learning via Transformation Functionscited by 26
  9. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  10. 2020Counterfactual Predictions under Runtime Confoundingcited by 22

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 13; 2023: 14; 2024: 18; 2025: 19; 2026: 8.

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  1. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  2. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  3. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  4. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  5. 2023Explicit Feature Interaction-aware Uplift Network for Online Marketingcited by 25
  6. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  7. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  8. 2023Causal Deep Learningcited by 20
  9. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  10. 2023Zero-shot causal learningcited by 17
  11. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  12. 2024DiffPO: A causal diffusion model for learning distributions of potential outcomescited by 17
38 more
  1. 2021Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxiescited by 16
  2. 2022Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretabilitycited by 15
  3. 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Timecited by 14
  4. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  5. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
  6. 2024Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environmentscited by 11
  7. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
  8. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  9. 2022Experimental Standards for Deep Learning in Natural Language Processing Researchcited by 9
  10. 2022Adversarial De-confounding in Individualised Treatment Effects Estimationcited by 9
  11. 2024Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effectscited by 7
  12. 2025Treatment Effect Estimation for Optimal Decision-Makingcited by 7
  13. 2022Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimationcited by 5
  14. 2023Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Datacited by 5
  15. 2024Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growthcited by 5
  16. 2021Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmarkcited by 4
  17. 2022Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regressioncited by 4
  18. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
  19. 2025Hybrid Meta-learners for Estimating Heterogeneous Treatment Effectscited by 4
  20. 2023Estimating Treatment Effects in Continuous Time with Hidden Confounderscited by 3
  21. 2023Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representationcited by 3
  22. 2024Benchmarking for Deep Uplift Modeling in Online Marketingcited by 3
  23. 2024PairNet: Training with Observed Pairs to Estimate Individual Treatment Effectcited by 3
  24. 2024Improve ROI with Causal Learning and Conformal Predictioncited by 3
  25. 2025A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatmentscited by 3
  26. 2022Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutionscited by 2
  27. 2023CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regressioncited by 2
  28. 2024Disentangle Estimation of Causal Effects from Cross-Silo Datacited by 2
  29. 2024Federated Learning for Estimating Heterogeneous Treatment Effectscited by 2
  30. 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimatorscited by 2
  31. 2024Defining Expertise: Applications to Treatment Effect Estimationcited by 2
  32. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  33. 2025CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networkscited by 2
  34. 2026Rank-Learner: Orthogonal Ranking of Treatment Effectscited by 2
  35. 2022NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimationcited by 1
  36. 2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
  37. 2024SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identificationcited by 1
  38. 2024Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disordercited by 1

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