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

February 18, 2024

Graph Out-of-Distribution Generalization via Causal Intervention
Qitian Wu, Fan Nie, Chenxiao Yang, Tianyi Bao, Junchi Yan
Strong Generalization Causal Inference Causal Intervention Graph Out of Distribution Generalization Ego Graph

February 16, 2024

Robust agents learn causal world models
Jonathan Richens, Tom Everitt
Causal Inference Causal Model Underlying Causal Robust Agent Causal World Model

February 12, 2024

Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
Md Musfiqur Rahman, Matt Jordan, Murat Kocaoglu
Causal Inference Causal Graph Causal Effect Conditional Generative External Sample Conditional Sampling

February 2, 2024

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai
Causal Inference Causal Discovery Causal Model Causal Approach

January 31, 2024

Continuous Treatment Effects with Surrogate Outcomes
Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan Rossi, Ritwik Sinha, Edward H. Kennedy
Causal Inference Relevant Covariates Selection Bias Surrogate Objective Continuous Treatment Effect Treatment Variable

January 19, 2024

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
Aoqi Zuo, Yiqing Li, Susan Wei, Mingming Gong
Causal Inference Causal Graph Constrained Optimization Fairness Intervention Causal Fairness Causal DAG Fair Prediction

January 16, 2024

Deep Learning-based Group Causal Inference in Multivariate Time-series
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Causal Inference Causal Structure Multivariate Time Series Causal Direction Deep Causal Causality Method

January 12, 2024

January 5, 2024

Neural Causal Abstractions
Kevin Xia, Elias Bareinboim
Causal Inference Causal Inference Task Causal Abstraction Neural Causal Model

December 24, 2023

The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
George Gui, Olivier Toubia
Large Language Model Causal Inference Challenge Task Human Behavior Conditional Average Treatment Effect Global Endogenous Variate LLM Simulation

December 23, 2023

December 22, 2023

Zero-shot Causal Graph Extrapolation from Text via LLMs
Alessandro Antonucci, Gregorio Piqué, Marco Zaffalon
Large Language Model Text Modality Causal Inference Causal Graph Causal Relation Causal View Ground Truth Causal Zero Shot Causal

December 14, 2023

Casual Social Media Use among the Youth: Effects on Online and Offline Political Participation
Mehdi Barati
Social Medium Mixed Effect Causal Inference Online Service

December 13, 2023

Double Machine Learning for Static Panel Models with Fixed Effects
Paul Clarke, Annalivia Polselli
Causal Inference Double Machine Learning Panel Data Fixed Effect