Cover Image

Estimating Treatment Effects in Networks using Domain Adversarial Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Caljon, Daan, Van Belle, Jente, Verbeke, Wouter
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Optimizing Treatment Allocation in the Presence of Interference
by: Caljon, Daan, et al.
Published: (2024)

Using dynamic loss weighting to boost improvements in forecast stability
by: Caljon, Daan, et al.
Published: (2024)

Stabilizing distribution-free probabilistic forecasts
by: Van Belle, Jente, et al.
Published: (2026)

Decision-centric fairness: Evaluation and optimization for resource allocation problems
by: De Vos, Simon, et al.
Published: (2025)

Metalearners for Ranking Treatment Effects
by: Vanderschueren, Toon, et al.
Published: (2024)

Uncertainty in Machine Learning
by: Weytjens, Hans, et al.
Published: (2025)

Is TabPFN the Silver Bullet for Insurance Pricing?
by: Deprez, Bruno, et al.
Published: (2026)

Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation
by: Bockel-Rickermann, Christopher, et al.
Published: (2024)

Logic Tensor Network-Enhanced Generative Adversarial Network
by: Upreti, Nijesh, et al.
Published: (2026)

Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
by: Deprez, Bruno, et al.
Published: (2024)

Inductive inference of gradient-boosted decision trees on graphs for insurance fraud detection
by: Vandervorst, Félix, et al.
Published: (2025)

Uplift modeling with continuous treatments: A predict-then-optimize approach
by: De Vos, Simon, et al.
Published: (2024)

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
by: Deprez, Bruno, et al.
Published: (2025)

CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
by: Dionelis, Nikolaos, et al.
Published: (2025)

The foundations of cost-sensitive causal classification
by: Verbeke, Wouter, et al.
Published: (2020)

Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
by: Deprez, Bruno, et al.
Published: (2025)

PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
by: Nagalapatti, Lokesh, et al.
Published: (2024)

Treatment Effect Estimation with Observational Network Data using Machine Learning
by: Emmenegger, Corinne, et al.
Published: (2022)

Using representation balancing to learn conditional-average dose responses from clustered data
by: Bockel-Rickermann, Christopher, et al.
Published: (2023)

Treatment of Statistical Estimation Problems in Randomized Smoothing for Adversarial Robustness
by: Voracek, Vaclav
Published: (2024)

Treatment Effect Estimation with Differentiated Networked Effect on Graph Data
by: Lin, Xiaofeng, et al.
Published: (2026)

Deep Disentangled Representation Network for Treatment Effect Estimation
by: Meng, Hui, et al.
Published: (2025)

Contrastive Adversarial Training for Unsupervised Domain Adaptation
by: Chen, Jiahong, et al.
Published: (2024)

Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery
by: Yurtman, Aras, et al.
Published: (2025)

Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect
by: Abdisa, Atomsa Gemechu, et al.
Published: (2025)

Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
by: Ahuja, Angad Singh
Published: (2026)

Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification
by: Wu, Yuan
Published: (2024)

Digital Shielding for Cross-Domain Wi-Fi Signal Adaptation using Relativistic Average Generative Adversarial Network
by: Avola, Danilo, et al.
Published: (2025)

Performative Drift Resistant Classification Using Generative Domain Adversarial Networks
by: Makowski, Maciej, et al.
Published: (2025)

Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
by: Hirata, Tomu, et al.
Published: (2025)

On the Noise Stability and Robustness of Adversarially Trained Networks on NVM Crossbars
by: Tao, Chun, et al.
Published: (2021)

Reliable Selection of Heterogeneous Treatment Effect Estimators
by: Guo, Jiayi, et al.
Published: (2025)

Treatment Effect Estimation for Optimal Decision-Making
by: Frauen, Dennis, et al.
Published: (2025)

Estimating Aleatoric Uncertainty in the Causal Treatment Effect
by: Xu, Liyuan, et al.
Published: (2026)

Federated Learning for Estimating Heterogeneous Treatment Effects
by: Makhija, Disha, et al.
Published: (2024)

Treatment Effect Estimation for Graph-Structured Targets
by: Harada, Shonosuke, et al.
Published: (2024)

Stochastic Adversarial Networks for Multi-Domain Text Classification
by: Wang, Xu, et al.
Published: (2024)

Optimal Transport for Treatment Effect Estimation
by: Wang, Hao, et al.
Published: (2023)

On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement
by: Khan, Ahmad Saeed, et al.
Published: (2024)

EEG-Based Inter-Patient Epileptic Seizure Detection Combining Domain Adversarial Training with CNN-BiLSTM Network
by: Tazaki, Rina, et al.
Published: (2025)