Search research

Topics & fields

Synthetic Data357 papers Synthetic Data233 papers Synthetic Data Augmentation155 papers Synthetic Dataset54 papers Synthetic Tabular Data42 papers Synthetic Training Data33 papers

2,101–2,130 of 2,587

Papers

Causal Inference for Sequential Settings under Interference and Latent Confounding
Jul 16, 2026Phevos Paschalidis, Constantinos Daskalakis, Devavrat ShahCausal Inferences Latent Confounders
Causal Intelligence for Constraint-Aware Intervention Design to Induce State Transitions
May 27, 2026Zixuan Song, Uwe Mueller, Dimitris V. ManatakisSubspace Intervention Framework Causal Reasoning
Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction
Mar 31, 2026L. Ghiringhelli, A. Zambon, G. Tiana
Disentangling Pictorial Cue Understanding from Language Bias in VLMs via Depth Ordering Task
Jul 1, 2026Yiqian Liu, Iuliia Kotseruba, John K. TsotsosVisual Perception
Knowledge-Graph Grounding Helps LLMs Only for Out-of-Training Knowledge: A Controlled Study on Clinical Question Answering
Jun 21, 2026Madhulatha Mandarapu, Sandeep KunkunuruMulti-Hop Question Answering Benchmarks Contextual Grounding
Geometrical fairness in graph neural networks
Jun 16, 2026Arturo Pérez-Peralta, Sandra Benítez-Peña, Blas Kolic +1Fairness-Aware Learning Graph Neural Networks
SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models
Jul 25, 2026Weijie Li, Yafei Song, Yongxiang Liu +7Synthetic Aperture Radar Multimodal Pretraining
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
Jul 13, 2026Tiberiu Musat, Tiago Pimentel, Nicolas Zucchet +1Transformer Architectures Trainable Parameter
Do Image Editing Models Understand Lighting?
Jun 25, 2026Tim Küchler, Johann-Friedrich Feiden, Matthias Nießner +1Lighting Image Editing
Document-Authored Control-Signal Impersonation: A Low-Cost Indirect Prompt Attack on RAG Safety Boundaries
Jun 8, 2026Jianguo ZhuRetrieval-Augmented Generation Systems Metadata
Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments
Jun 2, 2026Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu +4Multi-Turn Tool-Use Agent Outcome-Based Rewards
EIB-Net: Entropy-Guided Information Bottleneck for Generalizable AI-Generated Image Detection
Sep 24, 2026Zhida Zhang, Xinlei Ma, Jie CaoAi-Generated Image Detection
Online Sharp-Calibrated Bayesian Optimization
May 11, 2026Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1Bayesian Optimization Gaussian Process
Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation
Sep 15, 2026Chethana Prasad Kabgere, Shylaja SSFederated Graph Learning Federated Learning
Automated Perceptually-Motivated Assessment of Photographic Consistency in Paired Clinical Photographs: Pipeline Development and Internal Evaluation
Sep 14, 2026Derrick Lin, Samantha Rabinovich, Joclin Rabinovich +5Image Quality Assessment Inter-Rater Variability
Pathways of Visual Information Flow in Vision-Language Models
Jul 3, 2026Israfel Salazar, Stella Frank, Dan Oneata +2Visual Context Corrupted Observations
The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology
Jul 1, 2026Andrzej Szablewski, Gabriel Konar-Steenberg, Raffaello Fornasiere +2Model Interpretability Model Activations
SilentRetrieval: Hijacking Retrieval-Augmented Generation via Semantically-Preserving Adversarial Data Poisoning
May 27, 2026Jiachen QianRetrieval-Augmented Generation Systems Language Model Evasion Attacks
Counterfactual Explanations Under Concept Drift
May 17, 2026Marcin Kostrzewa, Jerzy Stefanowski, Maciej ZiębaCounterfactual Explanation Explainability
Flow Sampling: Learning to Sample from Unnormalized Densities via Denoising Conditional Processes
May 5, 2026Aaron Havens, Brian Karrer, Neta ShaulDiffusion Sampling Riemannian Manifolds
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Apr 26, 2026Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro CavalcanteGromov--Wasserstein Multi-View
Illuminating Visual Identity in Universal Multimodal Embeddings
Aug 3, 2026Jiawei Cao, Junyi Feng, Jiashen Hua +5Multimodal Embeddings Unified Multimodal Models
Generative-Model Predictive Planning for Navigation in Partially Observable Environments
Jun 17, 2026Thomas Quilter, Yifan Zhu, Guorui Quan +2Diffusion Planning Model-Free Reinforcement Learning
Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting
Apr 23, 2026Avinash Paliwal, Adithya Iyer, Shivin Yadav +2Controllable Video Generation Camera Control
Pheno-GS: Phenoscape-scale Geodesic Sinkhorn
Sep 23, 2026Alistair Wilkinson, Christopher J. Tape, Smita KrishnaswamyFarthest Point Sampling Heterogeneity
Learner-based Concept Drift Detection: Analysis and Evaluation
Jun 18, 2026Md Moman Ul Haque Khan, Samira SadaouiNon-Stationarity Machine Learning
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
Jun 4, 2026Sota AsanumaCausal Foundation Models Mechanistic Interpretability
Faithful Embeddings of Irregular and Asynchronous Data for Online Log-NCDEs
May 28, 2026Benjamin Walker, Alexandre Bloch, Lingyi Yang +2Neural Ordinary Differential Equations Latent Dynamics
The Impossibility Triangle of Long-Context Modeling
May 6, 2026Yan ZhouSequence Modeling Retrieval Recall
ArchSym: Detecting 3D-Grounded Architectural Symmetries in the Wild
Apr 24, 2026Hanyu Chen, Ruojin Cai, Steve Marschner +1Symmetry Structure-From-Motion

Jul 16, 2026 · cs.LGJ/K move · Enter open · S save

Causal Inference for Sequential Settings under Interference and Latent Confounding

Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

EECS, LIDS, CSAIL · MIT · EECS, CSAIL · EECS, IDSS, LIDS, SDSC

We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also impacted through an external field capturing the effects of its treatment as well as latent confounders. Similar to panel data literature, these latent confounders are modeled to have a low-rank factor structure. Our data is a single sample from this high-dimensional distribution. To estimate causal quantities of interest, we provide a computationally efficient method based on Maximum Pseudo-Likelihood Estimation (MPLE) for learning the model parameters. Under mild assumptions, we establish non-asymptotic consistency for parameter estimation and show this translates to faithful estimation of causal quantities of interest after sampling from the learned model. We demonstrate the efficacy of the method through synthetic experiments as well as a real-world case-study investigating causal effects of vaccine rates on COVID-19 death rates within US counties nationwide.

Causal Inferences Latent Confounders Interference Sequential

Open paper Ask about it PDF