arXiv Machine Learning

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.

arXiv Machine Learning

Aug 28

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.

By Xiaojing Du

benchmarks

arXiv AI

Aug 10

A primer on optimal transport for causal inference with observational data

arXiv:2503. 07811v3 Announce Type: replace-cross Abstract: The theory of optimal transportation has developed into a powerful and elegant framework for comparing probability distributions, with wide-ranging applications in all areas of science.

By Florian F Gunsilius

arXiv Machine Learning

Jun 8

Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach

arXiv:2606. 07483v1 Announce Type: new Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion.

By Lei Huang

diffusion safety

arXiv Machine Learning

Jun 5

StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis

arXiv:2606. 05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare.

By Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li, Lavdim Halilaj, Juergen Luettin

benchmarks

arXiv AI

Aug 21

Causal Inference under Interference with Learned Exposure Mappings

arXiv:2608. 19224v1 Announce Type: cross Abstract: Exposure mappings are often assumed to be known in causal spillover analyses.

By Cong Cao

arXiv Machine Learning

Jun 9

Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks

arXiv:2606. 08306v1 Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining.

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

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 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

3d ago

Identifying Causal Effects Using a Single Proxy Variable

The paper tackles the problem of estimating causal effects when an unobserved confounder is present. It assumes a single, possibly multi‑dimensional proxy variable for the confounder and knowledge of the mechanism that generates this proxy. Under the Single Proxy Identifiability of Causal Effects (SPICE) assumption, the authors prove that the error mechanism is complete and causal effects are identifiable, extending prior proxy‑based results to continuous, multi‑dimensional settings and more flexible functional forms. They also introduce SPICE‑Net, a neural‑network‑based framework for estimating causal effects applicable to both discrete and continuous treatments.

By Silvan Vollmer, Niklas Pfister, Sebastian Weichwald

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 18

Identifying Structural Biases from Causal Mechanism Shifts

arXiv:2606. 18834v1 Announce Type: new Abstract: Causal discovery methods commonly assume that all data is independently and identically distributed (i.

By Praharsh Nanavati, Jilles Vreeken, David Kaltenpoth

benchmarks safety

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