Disentangled Instrumental Variables for Causal Inference with Networked Observational Data

• URL: http://arxiv.org/abs/2602.07765v1
• Date: Sun, 08 Feb 2026 01:45:21 GMT
• Title: Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
• Authors: Zhirong Huang, Debo Cheng, Guixian Zhang, Yi Wang, Jiuyong Li, Shichao Zhang,
• Abstract summary: We propose a novel method for causal inference based on networked observational data with latent confounders.<n>DisIV exploits network homogeneity as an inductive bias and employs a structural disentanglement mechanism to extract individual-specific components that serve as latent IVs.
• Score: 19.568312273682768
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Instrumental variables (IVs) are crucial for addressing unobservable confounders, yet their stringent exogeneity assumptions pose significant challenges in networked data. Existing methods typically rely on modelling neighbour information when recovering IVs, thereby inevitably mixing shared environment-induced endogenous correlations and individual-specific exogenous variation, leading the resulting IVs to inherit dependence on unobserved confounders and to violate exogeneity. To overcome this challenge, we propose $\underline{Dis}$entangled $\underline{I}$nstrumental $\underline{V}$ariables (DisIV) framework, a novel method for causal inference based on networked observational data with latent confounders. DisIV exploits network homogeneity as an inductive bias and employs a structural disentanglement mechanism to extract individual-specific components that serve as latent IVs. The causal validity of the extracted IVs is constrained through explicit orthogonality and exclusion conditions. Extensive semi-synthetic experiments on real-world datasets demonstrate that DisIV consistently outperforms state-of-the-art baselines in causal effect estimation under network-induced confounding.

Related papers

• Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables [2.3213238782019316]
  We show that under standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic and jointly Gaussian, the treatment-outcome relationship becomes uniquely identifiable from observed data.<n>This enables counterfactual inference even in nonseparable models.<n>We implement our approach by training a normalizing flow to maximize the likelihood of the observed data, demonstrating accurate recovery of the underlying outcome function.
  arXiv  Detail & Related papers  (2025-08-02T11:24:03Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Robust Molecular Property Prediction via Densifying Scarce Labeled Data [53.24886143129006]
  In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel bilevel optimization approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.
  arXiv  Detail & Related papers  (2025-06-13T15:27:40Z)
• Geometry-Aware Instrumental Variable Regression [56.16884466478886]
  We propose a transport-based IV estimator that takes into account the geometry of the data manifold through data-derivative information. We provide a simple plug-and-play implementation of our method that performs on par with related estimators in standard settings.
  arXiv  Detail & Related papers  (2024-05-19T17:49:33Z)
• Conditional Instrumental Variable Regression with Representation Learning for Causal Inference [20.454641170078812]
  This paper studies the challenging problem of estimating causal effects from observational data, in the presence of unobserved confounders. We propose a non-linear regression with Confounding Balancing Representation Learning, CBRL.CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders.
  arXiv  Detail & Related papers  (2023-10-03T08:08:09Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Causal Inference with Conditional Instruments using Deep Generative Models [21.771832598942677]
  A standard IV is expected to be related to the treatment variable and independent of all other variables in the system. conditional IV (CIV) method has been proposed to allow a variable to be an instrument conditioning on a set of variables. We propose to learn the representations of a CIV and its conditioning set from data with latent confounders for average causal effect estimation.
  arXiv  Detail & Related papers  (2022-11-29T14:31:54Z)
• On the instrumental variable estimation with many weak and invalid instruments [1.837552179215311]
  We discuss the fundamental issue of computation in instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest properties", which is is equivalent to a sparse penalty structure, we investigate and prove the advantages of a surrogate-step identification method. We propose a surrogate-step selection estimation method that aligns with the sparse identification condition.
  arXiv  Detail & Related papers  (2022-07-07T01:31:34Z)
• Ancestral Instrument Method for Causal Inference without Complete Knowledge [0.0]
  Unobserved confounding is the main obstacle to causal effect estimation from observational data. Conditional IVs have been proposed to relax the requirement of standard IVs by conditioning on a set of observed variables. We develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.
  arXiv  Detail & Related papers  (2022-01-11T07:02:16Z)
• Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning [107.70165026669308]
  In offline reinforcement learning (RL) an optimal policy is learned solely from a priori collected observational data. We study a confounded Markov decision process where the transition dynamics admit an additive nonlinear functional form. We propose a provably efficient IV-aided Value Iteration (IVVI) algorithm based on a primal-dual reformulation of the conditional moment restriction.
  arXiv  Detail & Related papers  (2021-02-19T13:01:40Z)
• On Disentangled Representations Learned From Correlated Data [59.41587388303554]
  We bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data. We show that systematically induced correlations in the dataset are being learned and reflected in the latent representations. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
  arXiv  Detail & Related papers  (2020-06-14T12:47:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.