Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders

• URL: http://arxiv.org/abs/2508.09154v1
• Date: Tue, 05 Aug 2025 05:49:49 GMT
• Title: Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
• Abstract summary: Existing methods either address unobserved confounders while ignoring the simultaneous feedback, or account for feedback but under restrictive linear assumptions.<n>We propose DIG2RSI, a novel Deep learning framework which addresses both simultaneous feedback and unobserved confounding.<n>We prove consistency of our estimator under standard conditions, ensuring recovery of the true peer effect.
• Score: 16.96064437725128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating peer causal effects within complex real-world networks such as social networks is challenging, primarily due to simultaneous feedback between peers and unobserved confounders. Existing methods either address unobserved confounders while ignoring the simultaneous feedback, or account for feedback but under restrictive linear assumptions, thus failing to obtain accurate peer effect estimation. In this paper, we propose DIG2RSI, a novel Deep learning framework which leverages I-G transformation (matrix operation) and 2SRI (an instrumental variable or IV technique) to address both simultaneous feedback and unobserved confounding, while accommodating complex, nonlinear and high-dimensional relationships. DIG2RSI first applies the I-G transformation to disentangle mutual peer influences and eliminate the bias due to the simultaneous feedback. To deal with unobserved confounding, we first construct valid IVs from network data. In stage 1 of 2RSI, we train a neural network on these IVs to predict peer exposure, and extract residuals as proxies for the unobserved confounders. In the stage 2, we fit a separate neural network augmented by an adversarial discriminator that incorporates these residuals as a control function and enforces the learned representation to contain no residual confounding signal. The expressive power of deep learning models in capturing complex non-linear relationships and adversarial debiasing enhances the effectiveness of DIG2RSI in eliminating bias from both feedback loops and hidden confounders. We prove consistency of our estimator under standard regularity conditions, ensuring asymptotic recovery of the true peer effect. Empirical results on two semi-synthetic benchmarks and a real-world dataset demonstrate that DIG2RSI outperforms existing approaches.

Related papers

• When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects [1.049126606580198]
  We introduce a controlled contrast framework that indexes potential outcomes by own treatment, temporally aggregated peer exposure, and a post-assignment evolution summary.<n>We develop the Dynamic Network Doubly Robust estimator, DynaNet-DR, which combines a temporally factorized propensity with normalized augmentation.
  arXiv  Detail & Related papers  (2026-08-27T14:32:40Z)
• Mitigating Estimation Bias with Representation Learning in TD Error-Driven Regularization [4.784045060345404]
  This work introduces enhanced methods to achieve flexible bias control and stronger representation learning.<n>We propose three convex combination strategies, symmetric and asymmetric, that balance pessimistic estimates to mitigate overestimation and optimistic exploration via double actors.<n>To further improve performance, we integrate augmented state and action representations into the actor and critic networks.
  arXiv  Detail & Related papers  (2025-11-20T06:31:55Z)
• Adjustment for Confounding using Pre-Trained Representations [2.916285040262091]
  We investigate how latent features from pre-trained neural networks can be leveraged to adjust for sources of confounding.<n>We show that neural networks can achieve fast convergence rates by adapting to intrinsic notions of sparsity and dimension of the learning problem.
  arXiv  Detail & Related papers  (2025-06-17T09:11:17Z)
• Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
  Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.<n>In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.<n>We show that even moderate levels of information sharing significantly mitigate environment-specific errors.
  arXiv  Detail & Related papers  (2025-03-21T18:06:28Z)
• Fully Heteroscedastic Count Regression with Deep Double Poisson Networks [6.976150282812484]
  Deep Double Poisson Network (DDPN) is a novel neural discrete count regression model.<n>We show that DDPN exhibits robust regression properties similar to heteroscedastic Gaussian models.<n>Experiments on diverse datasets demonstrate that DDPN outperforms current baselines in accuracy, calibration, and out-of-distribution detection.
  arXiv  Detail & Related papers  (2024-06-13T16:02:03Z)
• Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning [24.63284452991301]
  We propose a doubly robust causal effect estimator under networked interference. Specifically, we generalize the targeted learning technique into the networked interference setting. We devise an end-to-end causal effect estimator by transforming the identified theoretical condition into a targeted loss.
  arXiv  Detail & Related papers  (2024-05-06T10:49:51Z)
• Contrastive variational information bottleneck for aspect-based sentiment analysis [36.83876224466177]
  We propose to reduce spurious correlations for aspect-based sentiment analysis (ABSA) via a novel Contrastive Variational Information Bottleneck framework (called CVIB) The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning. Our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization.
  arXiv  Detail & Related papers  (2023-03-06T02:52:37Z)
• Explicit Tradeoffs between Adversarial and Natural Distributional Robustness [48.44639585732391]
  In practice, models need to enjoy both types of robustness to ensure reliability. In this work, we show that in fact, explicit tradeoffs exist between adversarial and natural distributional robustness.
  arXiv  Detail & Related papers  (2022-09-15T19:58:01Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Unpaired Adversarial Learning for Single Image Deraining with Rain-Space Contrastive Constraints [61.40893559933964]
  We develop an effective unpaired SID method which explores mutual properties of the unpaired exemplars by a contrastive learning manner in a GAN framework, named as CDR-GAN. Our method performs favorably against existing unpaired deraining approaches on both synthetic and real-world datasets, even outperforms several fully-supervised or semi-supervised models.
  arXiv  Detail & Related papers  (2021-09-07T10:00:45Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Provably Efficient Causal Reinforcement Learning with Confounded Observational Data [135.64775986546505]
  We study how to incorporate the dataset (observational data) collected offline, which is often abundantly available in practice, to improve the sample efficiency in the online setting. We propose the deconfounded optimistic value iteration (DOVI) algorithm, which incorporates the confounded observational data in a provably efficient manner.
  arXiv  Detail & Related papers  (2020-06-22T14:49:33Z)
• Feature Purification: How Adversarial Training Performs Robust Deep Learning [66.05472746340142]
  We show a principle that we call Feature Purification, where we show one of the causes of the existence of adversarial examples is the accumulation of certain small dense mixtures in the hidden weights during the training process of a neural network. We present both experiments on the CIFAR-10 dataset to illustrate this principle, and a theoretical result proving that for certain natural classification tasks, training a two-layer neural network with ReLU activation using randomly gradient descent indeed this principle.
  arXiv  Detail & Related papers  (2020-05-20T16:56:08Z)

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.