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

January 31, 2023

Improving Open-Domain Dialogue Evaluation with a Causal Inference Model
Cat P. Le, Luke Dai, Michael Johnston, Yang Liu, Marilyn Walker, Reza Ghanadan
Causal Inference Open Domain Open Domain Dialogue

January 30, 2023

Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities
Connor T. Jerzak, Fredrik Johansson, Adel Daoud
Technical Challenge Causal Inference Emerging Opportunity Unobserved Confounders Latent Confounding Observational Study

January 25, 2023

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum, Weiran Yao, Chenghao Liu, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles
Time Series Causal Inference Causal Discovery Tabular Data Causal Analysis Causal Machine Ground Truth Causal

January 20, 2023

Causal Inference under Data Restrictions
Xiaoqing Tan
Causal Inference Conditional Average Treatment Effect Individualized Decision

January 17, 2023

Causal Falsification of Digital Twins
Rob Cornish, Muhammad Faaiz Taufiq, Arnaud Doucet, Chris Holmes
Causal Inference Digital Twin Identical Twin Multiple Trajectory

January 9, 2023

Subset verification and search algorithms for causal DAGs
Davin Choo, Kirankumar Shiragur
Causal Inference Causal Graph Directed Acyclic Graph Formal Verification Search Algorithm Ground Truth Causal

January 3, 2023

Continual Causal Effect Estimation: Challenges and Opportunities
Zhixuan Chu, Sheng Li
Technical Challenge Causal Inference Emerging Opportunity Causal Effect Estimation

January 2, 2023

December 27, 2022

Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering
M. Z. Naser
Causal Inference Causal Pattern Theoretical Physic Functional Mechanism Brief Review Philosophical View Geometric Regularity

December 22, 2022

Cross-Dataset Propensity Estimation for Debiasing Recommender Systems
Fengyu Li, Sarah Dean
Recommender System Causal Inference Movie Recommendation Propensity Score Selection Bias Debiased Recommendation Inverse Probability Weighting

December 20, 2022

Out-of-sample scoring and automatic selection of causal estimators
Egor Kraev, Timo Flesch, Hudson Taylor Lekunze, Mark Harley, Pere Planell Morell
Causal Inference Feature Selection Causal Effect Causal Model Instrumental Variable Scoring Property Automatic Selection

December 13, 2022

On the Relationship Between Explanation and Prediction: A Causal View
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith, Bernhard Schölkopf, Been Kim
Human Prediction Line by Line Explanation Causal Inference Human Relationship Explanation Method Causal View

December 12, 2022

Instrumental Variables in Causal Inference and Machine Learning: A Survey
Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Fei Wu
Machine Learning Timely Survey Causal Inference Instrumental Variable

December 9, 2022

December 8, 2022

December 7, 2022

Neighborhood Adaptive Estimators for Causal Inference under Network Interference
Alexandre Belloni, Fei Fang, Alexander Volfovsky
Causal Inference Causal Effect Catastrophic Interference Average Treatment Effect Interdependent Network Causal Assumption Network Interference Adaptive Neighborhood

December 6, 2022

Causal Inference via Style Transfer for Out-of-distribution Generalisation
Toan Nguyen, Kien Do, Duc Thanh Nguyen, Bao Duong, Thin Nguyen
Strong Generalization Causal Inference Style Transfer Distribution Generalization Unobserved Confounders