mlsanityData to 14 Oct 2026

Estimating Treatment Effects with Independent Component Analysis

arXiv 2507.16467 ↗First posted 22 Jul 2025stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

Independent Component Analysis (ICA) uses a measure of non-Gaussianity to identify latent sources from data and estimate their mixing coefficients (Shimizu et al., 2006). Meanwhile, higher-order Orthogonal Machine Learning (OML) exploits non-Gaussian treatment noise to provide more accurate estimates of treatment effects in the presence of confounding nuisance effects (Mackey et al., 2018). Remarkably, we find that the two approaches rely on the same moment conditions for consistent estimation. We then seize upon this connection to show how ICA can be effectively used for treatment effect estimation. Specifically, we prove that linear ICA can consistently estimate multiple treatment effects, even in the presence of Gaussian confounders, and identify regimes in which ICA is provably more sample-efficient than OML for treatment effect estimation. Our synthetic demand estimation experiments confirm this theory and demonstrate that linear ICA can accurately estimate treatment effects even in the presence of nonlinear nuisance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 72 references that are in the map. First posted on arXiv 22 Jul 2025.

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32 of 72 references.

References · 32

  1. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
  2. 2019Variational Autoencoders and Nonlinear ICA: A Unifying Frameworkcited by 503
  3. 2018Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learningcited by 282
  4. 2016Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICAcited by 273
  5. 2020Weakly-Supervised Disentanglement Without Compromisescited by 239
  6. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  7. 2020ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICAcited by 102
  8. 2019The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICAcited by 86
  9. 2021Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICAcited by 64
  10. 2023Causal Component Analysiscited by 56
  11. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  12. 2023Scalable Causal Discovery with Score Matchingcited by 45
20 more
  1. 2016Double/Debiased Machine Learning for Treatment and Causal Parameterscited by 43
  2. 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 43
  3. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  4. 2024Cross-Entropy Is All You Need To Invert the Data Generating Processcited by 31
  5. 2017Orthogonal Machine Learning: Power and Limitationscited by 28
  6. 2020Independent Innovation Analysis for Nonlinear Vector Autoregressive Processcited by 19
  7. 2024Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learningcited by 16
  8. 2020Estimation of Causal Effects of Multiple Treatments in Observational Studies with a Binary Outcomecited by 13
  9. 2023Large Dimensional Independent Component Analysis: Statistical Optimality and Computational Tractabilitycited by 12
  10. 2024Do Finetti: On Causal Effects for Exchangeable Datacited by 11
  11. 2024Score matching through the roof: linear, nonlinear, and latent variables causal discoverycited by 10
  12. 2023An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysiscited by 9
  13. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
  14. 2025It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimationcited by 6
  15. 2024On the Identifiability of Sparse ICA without Assuming Non-Gaussianitycited by 5
  16. 2023Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediatorscited by 4
  17. 2025Double Machine Learning for Causal Inference under Shared-State Interferencecited by 4
  18. 2025Causal Effect Identification in Heterogeneous Environments from Higher-Order Momentscited by 2
  19. 2025Simultaneous Estimation of Multiple Treatment Effects from Observational Studiescited by 1
  20. 2025Double machine learning to estimate the effects of multiple treatments and their interactionscited by 1

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