Underlying Causal

Underlying causal inference aims to uncover the true causal relationships between variables, moving beyond mere correlations to understand how changes in one variable directly affect others. Current research focuses on developing robust methods for causal discovery, particularly in high-dimensional and complex systems, employing techniques like Bayesian networks, neural networks, and optimal transport to learn causal structures from observational and interventional data. These advancements are crucial for improving the reliability and interpretability of machine learning models, enabling more effective decision-making in diverse fields such as healthcare, policy, and engineering.

Papers

June 1, 2023

Nonparametric Identifiability of Causal Representations from Unknown Interventions
Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
Causal Representation Learning Latent Causal Causal Representation Underlying Causal Unknown Intervention

March 16, 2023

Causal Temporal Graph Convolutional Neural Networks (CTGCN)
Abigail Langbridge, Fearghal O'Donncha, Amadou Ba, Fabio Lorenzi, Christopher Lohse, Joern Ploennigs
Causal Discovery Causal Model Underlying Causal

February 28, 2023

Practical Algorithms for Orientations of Partially Directed Graphical Models
Malte Luttermann, Marcel Wienöbst, Maciej Liśkiewicz
Practical Algorithm Causal Discovery Causal Model Probabilistic Graphical Model Underlying Causal Orientation Feature

February 10, 2023

On the Interventional Kullback-Leibler Divergence
Jonas Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf
Causal Model Distributional Assumption Underlying Causal Causal Variable Interventional Distribution

January 21, 2023

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
Zeyu Tang, Yatong Chen, Yang Liu, Kun Zhang
Fairness Notion Underlying Causal Long Term Fairness Dynamic Fairness

November 18, 2022

Model-based Causal Bayesian Optimization
Scott Sussex, Anastasiia Makarova, Andreas Krause
Bayesian Optimization Underlying Causal Causal Bayesian Optimization

October 24, 2022

Learning Latent Structural Causal Models
Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke, Tristan Deleu, Stefan Bauer, Derek Nowrouzezahrai, Samira Ebrahimi Kahou
Causal Model Causal Learning Underlying Causal Structural Causal

October 16, 2022

CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, Jundong Li
Graph Drawing Counterfactual Explanation Generative Question High Quality Counterfactuals Underlying Causal Unseen Graph Class Enhanced Attentive Response

August 23, 2022

Towards a Better Microcredit Decision
Mengnan Song, Jiasong Wang, Suisui Su
Underlying Causal Sequential Dependency

July 18, 2022

A Meta-Reinforcement Learning Algorithm for Causal Discovery
Andreas Sauter, Erman Acar, Vincent François-Lavet
Causal Discovery Causal Graph Causal Structure Underlying Causal

June 25, 2022

Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty
Christopher Tran, Elena Zheleva
Heterogeneous Treatment Effect Underlying Causal Heterogeneous Effect

June 17, 2022

Reframed GES with a Neural Conditional Dependence Measure
Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen
Causal Inference Causal Discovery Causal Structure Underlying Causal Convolutional Conditional Neural Process Greedy Equivalence Search

June 1, 2022

Feature Selection for Discovering Distributional Treatment Effect Modifiers
Yoichi Chikahara, Makoto Yamada, Hisashi Kashima
Feature Selection Treatment Effect Feature Importance Conditional Average Treatment Effect Underlying Causal Average Treatment Effect

April 7, 2022

A Framework for Following Temporal Logic Instructions with Unknown Causal Dependencies
Duo Xu, Faramarz Fekri
Deep Reinforcement Learning New Framework Temporal Logic Hierarchical Reinforcement Learning Inductive Logic Programming Underlying Causal Transition Model

April 2, 2022

Do learned representations respect causal relationships?
Lan Wang, Vishnu Naresh Boddeti
Meaningful Representation Causal Relationship Causal Relation Underlying Causal Observational Causal

February 20, 2022

Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables
Matthew J. Vowels
Machine Learning Causal Structure Fundamental Limitation Causal Pattern Underlying Causal Explainability Technique Causal Objective Function

December 8, 2021

Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
Soroush Saghafian
Unobserved Confounders Underlying Causal Dynamic Treatment Regime Optimal Treatment