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Nonparametric methods for doubly robust estimation of continuous treatment effects

arXiv 1507.00747 ↗First posted 2 Jul 2015stat.MEcited by 62

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Abstract (arXiv)

Continuous treatments (e.g., doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve, and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and give a procedure for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2015.

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Citing papers per year: 2021: 10; 2022: 11; 2023: 5; 2024: 18; 2025: 9; 2026: 9.

62 papers.

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  1. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
  2. 2024Causal machine learning for predicting treatment outcomescited by 73
  3. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  4. 2021Causal Effect Inference for Structured Treatmentscited by 38
  5. 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learningcited by 19
  6. 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
  7. 2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
  8. 2025Doubly Robust Alignment for Large Language Modelscited by 11
  9. 2022Comparison of meta-learners for estimating multi-valued treatment heterogeneous effectscited by 10
  10. 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effectcited by 10
  11. 2021End-to-End Balancing for Causal Continuous Treatment-Effect Estimationcited by 9
  12. 2022A Neural Mean Embedding Approach for Back-door and Front-door Adjustmentcited by 8
38 more
  1. 2024Probabilities of Causation for Continuous and Vector Variablescited by 8
  2. 2024Causal Machine Learning for Cost-Effective Allocation of Development Aidcited by 6
  3. 2024Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimationcited by 6
  4. 2021Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curvescited by 5
  5. 2024Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameterscited by 5
  6. 2021Automatic Double Machine Learning for Continuous Treatment Effectscited by 4
  7. 2021Multiply Robust Causal Mediation Analysis with Continuous Treatmentscited by 4
  8. 2022Kernel methods for long term dose response curvescited by 4
  9. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  10. 2023Doubly Robust Proximal Causal Learning for Continuous Treatmentscited by 4
  11. 2024Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weightscited by 4
  12. 2024Fast Estimation of Partial Dependence Functions using Treescited by 4
  13. 2025Double Machine Learning for Causal Inference under Shared-State Interferencecited by 4
  14. 2022GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplacecited by 3
  15. 2022CausalEGM: a general causal inference framework by encoding generative modelingcited by 3
  16. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  17. 2022Doubly Robust Kernel Statistics for Testing Distributional Treatment Effectscited by 2
  18. 2024Interventional Processes for Causal Uncertainty Quantificationcited by 2
  19. 2025Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combinationcited by 2
  20. 2026Causal methods for LLM development and evaluationcited by 2
  21. 2021Causal inference for observational longitudinal studies using deep survival modelscited by 1
  22. 2021Stochastic Intervention for Causal Inference via Reinforcement Learningcited by 1
  23. 2021Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposurescited by 1
  24. 2022Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviourcited by 1
  25. 2023Data-Driven Estimation of Heterogeneous Treatment Effectscited by 1
  26. 2024Continuous Treatment Effects with Surrogate Outcomescited by 1
  27. 2024New User Event Prediction Through the Lens of Causal Inferencecited by 1
  28. 2024A Density Ratio Super Learnercited by 1
  29. 2024Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learningcited by 1
  30. 2025Double Debiased Machine Learning for Mediation Analysis with Continuous Treatmentscited by 1
  31. 2025Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learningcited by 1
  32. 2025Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyondcited by 1
  33. 2026Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regressioncited by 1
  34. 2021Causal Bias Quantification for Continuous Treatmentscited by 0
  35. 2022Statistical Learning for Individualized Asset Allocationcited by 0
  36. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  37. 2024Causal Machine Learning for Moderation Effectscited by 0
  38. 2024Limits of Approximating the Median Treatment Effectcited by 0

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