mlsanityData to 7 Oct 2026

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

arXiv 2405.04715 ↗First posted 7 May 2024math.ST cs.LG stat.MEcited by 7

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Abstract (arXiv)

Pursuing causality from data is a fundamental problem in scientific discovery, treatment intervention, and transfer learning. This paper introduces a novel algorithmic method for addressing nonparametric invariance and causality learning in regression models across multiple environments, where the joint distribution of response variables and covariates varies, but the conditional expectations of outcome given an unknown set of quasi-causal variables are invariant. The challenge of finding such an unknown set of quasi-causal or invariant variables is compounded by the presence of endogenous variables that have heterogeneous effects across different environments. The proposed Focused Adversarial Invariant Regularization (FAIR) framework utilizes an innovative minimax optimization approach that drives regression models toward prediction-invariant solutions through adversarial testing. Leveraging the representation power of neural networks, FAIR neural networks (FAIR-NN) are introduced for causality pursuit. It is shown that FAIR-NN can find the invariant variables and quasi-causal variables under a minimal identification condition and that the resulting procedure is adaptive to low-dimensional composition structures in a non-asymptotic analysis. Under a structural causal model, variables identified by FAIR-NN represent pragmatic causality and provably align with exact causal mechanisms under conditions of sufficient heterogeneity. Computationally, FAIR-NN employs a novel Gumbel approximation with decreased temperature and a stochastic gradient descent ascent algorithm. The procedures are demonstrated using simulated and real-data examples.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 36 of its 89 references that are in the map. First posted on arXiv 7 May 2024.

Builds on

36 of 89 references.

References · 36

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24 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
  2. 2025CINDES: Classification induced neural density estimator and simulatorcited by 1
  3. 2026Optimal use of a black-box learner in semiparametric estimationcited by 1
  4. 2025Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglementcited by 0
  5. 2026Beyond identifiability: Learning causal representations with few environments and finite samplescited by 0
  6. 2026How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?cited by 0
  7. 2026Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAScited by 0