mlsanityData to 7 Oct 2026

Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach

arXiv 2206.09186 ↗First posted 18 Jun 2022cs.LG stat.MEcited by 9

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Abstract (arXiv)

We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error. We do so by generalizing estimation in the instrumental variable setting. Despite significant work on regression with measurement error, additionally handling unobserved confounding in the continuous setting is non-trivial: we have seen little prior work. As a by-product of our investigation, we clarify a connection between mean embeddings and characteristic functions, and how learning one simultaneously allows one to learn the other. This opens the way for kernel method research to leverage existing results in characteristic function estimation. Finally, we empirically show that our proposed method, MEKIV, improves over baselines and is robust under changes in the strength of measurement error and to the type of error distributions.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 18 Jun 2022.

Builds on

15 of 40 references.

References · 15

  1. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  3. 2015On the Equivalence between Kernel Quadrature Rules and Random Feature Expansionscited by 136
  4. 2019Kernel Instrumental Variable Regressioncited by 118
  5. 2019Deep Generalized Method of Moments for Instrumental Variable Analysiscited by 88
  6. 2013Counterfactual Probabilities: Computational Methods, Bounds and Applicationscited by 67
  7. 2020Learning Deep Features in Instrumental Variable Regressioncited by 65
  8. 2021Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restrictioncited by 54
  9. 2015Optimal Rates for Random Fourier Featurescited by 38
  10. 2015Optimal Sup-norm Rates and Uniform Inference on Nonlinear Functionals of Nonparametric IV Regressioncited by 30
  11. 2018Causal Inference with Noisy and Missing Covariates via Matrix Factorizationcited by 30
  12. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
3 more
  1. 2021Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacycited by 18
  2. 2020Partial Identifiability in Discrete Data With Measurement Errorcited by 10
  3. 2021Operationalizing Complex Causes: A Pragmatic View of Mediationcited by 8

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Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 1.

9 papers.

  1. 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effectcited by 10
  2. 2023Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedbackcited by 7
  3. 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Datacited by 4
  4. 2023Robust Bayesian Inference for Measurement Error Misspecification: The Berkson and Classical Casescited by 2
  5. 2023Causal Effect Regularization: Automated Detection and Removal of Spurious Attributescited by 1
  6. 2025ActiveCQ: Active Estimation of Causal Quantitiescited by 1
  7. 2022Nonlinear Causal Discovery via Kernel Anchor Regressioncited by 0
  8. 2022Using uncertainty-aware machine learning models to study aerosol-cloud interactionscited by 0
  9. 2023Identifiable causal inference with noisy treatment and no side informationcited by 0