arXiv Machine Learning

When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects

The paper introduces a controlled contrast framework for estimating peer effects when interaction graphs evolve, indexing potential outcomes by own treatment, temporally aggregated peer exposure, and a post‑assignment evolution summary. It proposes the Dynamic Network Doubly Robust estimator (DynaNet‑DR), which uses a temporally factorized propensity and normalized augmentation to achieve consistency under standard causal assumptions. Semi‑synthetic benchmarks on real temporal graph sequences demonstrate that DynaNet‑DR achieves favorable estimation accuracy compared to other methods, and an observational study on MathOverflow illustrates its practical application.

arXiv Machine Learning

Aug 20

Transportable Causal Effect Estimation across Networks under Interference

The paper introduces TranCE, a doubly‑robust algorithm for estimating causal effects when an intervention is applied to one network but the interest lies in another, differing network. By extending selection diagrams to capture covariate and structural network shifts, the authors derive transport formulas for direct, spillover, and total effects, and validate the method on semi‑synthetic social‑network benchmarks and a real weather‑insurance field experiment.

By Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le

benchmarks

arXiv AI

Jun 2

COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs

arXiv:2606. 00700v1 Announce Type: cross Abstract: Online link recommendation on evolving graphs is performative: by choosing which candidate links to show users, the system changes which links form and what feedback it later observes.

By Sheng'en Li, Dongmian Zou

safety

arXiv AI

Jun 2

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data

arXiv:2605. 24358v3 Announce Type: replace-cross Abstract: Estimating individual treatment effect (ITE) from observational graph data is crucial for decision-making in the fields such as commerce and medicine.

By Xiaofeng Lin, Han Bao, Hisashi Kashima

arXiv Machine Learning

Aug 4

Estimating Treatment Effects in Networks under Unknown Exposure Mappings

arXiv:2510. 21457v2 Announce Type: replace Abstract: Estimating heterogeneous treatment effects in network settings is complicated by interference, meaning that the outcome of an instance can be influenced by the treatment status of others.

By Daan Caljon, Jente Van Belle, Wouter Verbeke

safety

arXiv Statistics ML

Sep 22

Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach

arXiv:2609.22383v1 Announce Type: cross Abstract: Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument gen...

By Zixuan Yao, Guosheng Yin

arXiv AI

Jun 17

SP-GCRL: Influence Maximization on Incomplete Social Graphs

arXiv:2605. 12513v2 Announce Type: replace-cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics.

By Haohua Niu, Yuxuan Yang, Lingfeng Zhang, Hao Li, Jiao Liang, Zongfu Luo, Luca Rossi

diffusion reinforcement-learning

arXiv Machine Learning

Sep 15

Conformal Individual Treatment Effect Estimation under Networked Interference

arXiv:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual trea...

By Matteo Zecchin, Osvaldo Simeone

arXiv AI

Jun 15

Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation

arXiv:2505. 17961v4 Announce Type: replace-cross Abstract: Causal inference typically assumes centralized access to individual-level data.

By R\'emi Khellaf, Aur\'elien Bellet, Julie Josse

safety

arXiv AI

Jun 12

Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark

arXiv:2606. 12581v1 Announce Type: cross Abstract: Real-world networks are inherently incomplete, noisy, and dynamically evolving, making it difficult to capture all actors and their relationships.

By Mateusz Stolarski, Micha{\l} Czuba, Piotr Bielak, Piotr Br\'odka

benchmarks

arXiv AI

Jul 16

Partially Observed Structural Causal Models

arXiv:2605. 03268v2 Announce Type: replace-cross Abstract: Here we introduce Partially Observed Structural Causal Models (POSCMs) as an extension of structural causal models (SCMs) to settings where upstream contexts co-determine both the interaction structure and downstream mechanisms on observed variables.

By Turan Orujlu, Jordan Matelsky, Martin V. Butz, Charley M. Wu, Konrad P. Kording

arXiv Machine Learning

Jun 26

Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding

arXiv:2606. 27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance discriminative power and debiasing under unobserved confounding scenarios.

By Haoran Zhang, Chuanpu Li, Yuxin Fu, Bin Tong, Guan Wang, Bo Zheng, Feng Zhou

rag benchmarks safety

arXiv AI

Jul 20

Are Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective

arXiv:2510. 05750v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in node classification.

By Xiao Yang, Xuejiao Zhao, Zhiqi Shen

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