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

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

August 11, 2022

Valid Inference after Causal Discovery
Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
Scientific Inference Causal Inference Causal Discovery Causal Effect Estimation

August 8, 2022

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
Sérgio Machado, Anirudh Sridhar, Paulo Gil, Jorge Henriques, José M. F. Moura, Augusto Santos
Causal Inference Dynamical System Deep Learning Approach Partial Observability Graph Structure Learning Synthetic Network

July 29, 2022

Tangential Wasserstein Projections
Florian Gunsilius, Meng Hsuan Hsieh, Myung Jin Lee
Causal Inference Generalized Geodesic Wasserstein Space Probability Measure Multivariate Probability Distribution Projection Robust Wasserstein

July 19, 2022

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
Kailiang Zhong, Fengtong Xiao, Yan Ren, Yaorong Liang, Wenqing Yao, Xiaofeng Yang, Ling Cen
Causal Inference Treatment Effect Individual Treatment Effect Treatment Effect Estimation Treatment Selection Bias

July 11, 2022

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions
Heiner Kremer, Jia-Jie Zhu, Krikamol Muandet, Bernhard Schölkopf
Causal Inference Conditional Moment Restriction

July 10, 2022

On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Zhuangyan Fang, Ruiqi Zhao, Yue Liu, Yangbo He
Financial Application Causal Inference Individual Representation Directed Acyclic Graph Causal Knowledge Causal DAG Explicitly Mentioned Causal Fact

July 6, 2022

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property
Sridhar Mahadevan
Causal Inference K$ Universality Fundamental Diagram Actual Causality

July 4, 2022

June 20, 2022

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Jinkun Lin, Anqi Zhang, Mathias Lecuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen
Deep Learning Model Training Data Mixed Effect Causal Inference Shapley Value Randomized Experiment LASSO Regression

Causal Inference

Papers

Drawing Causal Inferences About Performance Effects in NLP

Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory

Normalizing Flows for Interventional Density Estimation

An evaluation framework for comparing causal inference models

Virtual Control Group: Measuring Hidden Performance Metrics

Meta-Causal Feature Learning for Out-of-Distribution Generalization

Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling

Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance

Network inference via process motifs for lagged correlation in linear stochastic processes

Collaborative causal inference on distributed data

Valid Inference after Causal Discovery

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

Tangential Wasserstein Projections

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Functional Generalized Empirical Likelihood Estimation for Conditional Moment Restrictions

On the Representation of Causal Background Knowledge and its Applications in Causal Inference

On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property

A Causal Approach for Business Optimization: Application on an Online Marketplace

Breaking Feedback Loops in Recommender Systems with Causal Inference

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments