Causal Inference
Causal inference aims to determine cause-and-effect relationships from data, going beyond mere correlations to understand how interventions impact outcomes. Current research heavily focuses on addressing challenges like confounding (the influence of unobserved variables), particularly in high-dimensional data and complex treatments (e.g., text, sequences of actions), employing methods such as structural causal models, Bayesian Additive Regression Trees (BART), and various neural network architectures including Graph Neural Networks (GNNs). These advancements are crucial for improving the reliability of causal conclusions across diverse fields, from medicine and economics to personalized interventions and policy-making.
Papers
Learning Representations of Instruments for Partial Identification of Treatment Effects
Jonas Schweisthal, Dennis Frauen, Maresa Schröder, Konstantin Hess, Niki Kilbertus, Stefan Feuerriegel
DiffPO: A causal diffusion model for learning distributions of potential outcomes
Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal, Stefan Feuerriegel
Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
Alec F. Diallo, Vaishak Belle, Paul Patras
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality
Guanyu Zhou, Yibo Yan, Xin Zou, Kun Wang, Aiwei Liu, Xuming Hu
Causal Reasoning in Software Quality Assurance: A Systematic Review
Luca Giamattei, Antonio Guerriero, Roberto Pietrantuono, Stefano Russo
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning
Pengfei Shi, Wei Zhong, Xinyu Zhang, Ningtao Wang, Xing Fu, Weiqiang Wang, Yin Jin