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
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
Jitao Wang, Chengchun Shi, John D. Piette, Joshua R. Loftus, Donglin Zeng, Zhenke Wu
Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing
Xiaofeng Xiao, Khawlah Alharbi, Pengyu Zhang, Hantang Qin, Xubo Yue
Do Large Language Models Show Biases in Causal Learning?
Maria Victoria Carro, Francisca Gauna Selasco, Denise Alejandra Mester, Margarita Gonzales, Mario A. Leiva, Maria Vanina Martinez, Gerardo I. Simari
Learning Structural Causal Models from Ordering: Identifiable Flow Models
Minh Khoa Le, Kien Do, Truyen Tran
Disentangled Representation Learning for Causal Inference with Instruments
Debo Cheng (1), Jiuyong Li (1), Lin Liu (1), Ziqi Xu (2), Weijia Zhang (3), Jixue Liu (1), Thuc Duy Le (1) ((1) UniSA STEM, University of South Australia, (2) School of Computing Technologies, RMIT University, and (3) School of Information and Physical Sciences, University of Newcastle)
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
Binbin Hu, Zhicheng An, Zhengwei Wu, Ke Tu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Yufei Feng, Jiawei Chen