mlsanityData to 14 Oct 2026

Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects

arXiv 2609.33740 ↗First posted 27 Sep 2026stat.ML cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dimensional covariates. We propose the Weighted Spline-Expanded Network (WSENet), an end-to-end neural framework that addresses these challenges by combining covariate balancing, structured treatment embedding, and bias-corrected outcome estimation. WSENet first applies Distance Covariate Optimal Weights to induce distributional independence between covariates and treatment without relying on parametric models. It then learns the conditional outcome via a structured network that fuses outcome-relevant representations of covariates with a spline-expanded treatment input, enabling smooth and flexible modeling of the dose-response relationship. To mitigate residual bias, we introduce Weighted Targeted Regularization, a correction technique based on efficient influence functions that yields a doubly robust estimator. Extensive evaluations on semi-synthetic and real-world datasets, including high-dimensional genomic and environmental health data, demonstrate that WSENet consistently outperforms existing baselines in both accuracy and stability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 62 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

23 of 62 references.

References · 23

  1. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  3. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  4. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  5. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 64
  6. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 61
  7. 2018Policy Evaluation and Optimization with Continuous Treatmentscited by 58
  8. 2019Semi-Parametric Efficient Policy Learning with Continuous Actionscited by 26
  9. 2018Visually Communicating and Teaching Intuition for Influence Functionscited by 19
  10. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  11. 2020Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settingscited by 14
  12. 2019Sensitivity analysis via the proportion of unmeasured confoundingcited by 13
11 more
  1. 2023Adversarially Balanced Representation for Continuous Treatment Effect Estimationcited by 10
  2. 2020Energy Balancing of Covariate Distributionscited by 9
  3. 2021End-to-End Balancing for Causal Continuous Treatment-Effect Estimationcited by 9
  4. 2023Covariate balancing using the integral probability metric for causal inferencecited by 9
  5. 2023Quasi-optimal Reinforcement Learning with Continuous Actionscited by 7
  6. 2019On Robustness of Individualized Decision Rulescited by 6
  7. 2021Independence weights for causal inference with continuous treatmentscited by 4
  8. 2024Incremental effects for continuous exposurescited by 4
  9. 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanismscited by 3
  10. 2025Reinforcement Learning with Continuous Actions Under Unmeasured Confoundingcited by 3
  11. 2024Nonparametric Inference on Dose-Response Curves Without the Positivity Conditioncited by 2

Built on by

Not cited yet.