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

October 8, 2022

Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition
Junhao Zheng, Zhanxian Liang, Haibin Chen, Qianli Ma
Continual LEArning Entity Recognition Catastrophic Forgetting Causal Inference General Class Causal Feature Distillation

October 6, 2022

Causal Inference for Chatting Handoff
Shanshan Zhong, Jinghui Qin, Zhongzhan Huang, Daifeng Li
Multi Task Learning Causal Inference Causal Graph Robot Handover Event Causality Identification

September 29, 2022

September 25, 2022

Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies
Mauricio Tec, James Scott, Corwin Zigler
Representation Learning Causal Inference Causal Effect Earth System Text Based Confounders Spatial Interaction Non Confounding Covariates

September 15, 2022

September 14, 2022

Drawing Causal Inferences About Performance Effects in NLP
Sandra Wankmüller
Natural Language Causal Inference NLP Field NLP Research

September 13, 2022

August 31, 2022

An evaluation framework for comparing causal inference models
Niki Kiriakidou, Christos Diou
Causal Inference Causal Effect Causal Effect Estimation Evaluation Framework Heterogeneous Effect

August 27, 2022

Virtual Control Group: Measuring Hidden Performance Metrics
Moshe Tocker
Causal Inference Performance Metric False Positive Rate

August 22, 2022

Meta-Causal Feature Learning for Out-of-Distribution Generalization
Yuqing Wang, Xiangxian Li, Zhuang Qi, Jingyu Li, Xuelong Li, Xiangxu Meng, Lei Meng
Causal Inference Distribution Generalization Causal Learning

August 21, 2022

Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
Ryoma Sato, Makoto Yamada, Hisashi Kashima
Causal Inference Counterfactual Analysis Software Citation Counterfactual Distribution Counterfactual Feature Research Impact

August 19, 2022

Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance
Satyam Kumar, Vadlamani Ravi
Application Proficiency High Explainability Causal Inference Causal Pattern Actual Cause Causal Query Insurance Industry

August 18, 2022

Network inference via process motifs for lagged correlation in linear stochastic processes
Alice C. Schwarze, Sara M. Ichinaga, Bingni W. Brunton
Causal Inference Total Correlation Granger Causality Network Inference Vector Autoregression

August 16, 2022

Collaborative causal inference on distributed data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai
Raw Data Causal Inference Private Data Causal Direction Intermediate Representation Data Collaboration