Causal Direction

Causal direction research aims to determine the cause-and-effect relationships between variables, moving beyond simple correlations to understand underlying mechanisms. Current research focuses on developing robust methods for causal discovery from observational data, often employing techniques like Bayesian networks, structural causal models, and graph neural networks, as well as leveraging large language models to extract causal information from text and data. This field is crucial for advancing scientific understanding across diverse disciplines and informing data-driven decision-making in areas such as healthcare, economics, and engineering, by enabling more accurate predictions and interventions.

Papers

September 25, 2023

Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors
Di Liang, Nian Shao, Xiaofei Li
Causal Direction Neural Diarization Frame Consistency Attractor Network End to End Speaker Diarization Speaker Attractor

August 11, 2023

Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks
Ricardo Cannizzaro, Jonathan Routley, Lars Kunze
Human Prediction Robot Person Line by Line Explanation Causal Inference Causal Model Real Robot Causal Direction Pick and Place Action Selection

July 11, 2023

Reducing Causality to Functions with Structural Models
Tianyi Miao
Causal Pattern Simple Function Multi Scenario Causal Direction Actual Cause Causal View Structure Based Model

June 5, 2023

Bivariate Causal Discovery using Bayesian Model Selection
Anish Dhir, Samuel Power, Mark van der Wilk
Causal Discovery Causal Direction Causal Assumption Bayesian Model Selection

May 31, 2023

Active causal structure learning with advice
Davin Choo, Themis Gouleakis, Arnab Bhattacharyya
Learning Algorithm Worst Case Causal Direction Advice Quality Temporal Causal Ancestral Graph

May 30, 2023

May 21, 2023

Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Raw Data Causal Relation Causal Direction Observational Data Diverse Equation Observational Causal

May 10, 2023

Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts
Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt
Native Robustness Distribution Shift Causal Direction Prediction Model Model Stability

April 25, 2023

Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
Christo Kurisummoottil Thomas, Walid Saad, Yong Xiao
Imitation Learning Digital Twin Causal Structure Semantic Communication Causal Direction

March 14, 2023

January 27, 2023

Epsilon-Identifiability of Causal Quantities
Ang Li, Scott Mueller, Judea Pearl
Causal Effect Causal Direction Actual Cause Partial Identifiability Counterfactual Distribution Epsilon Identifiability

January 26, 2023

Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
Xiangyu Sun, Oliver Schulte
Feature Selection Causal Discovery Maximum Likelihood Causal Direction Cause Effect Independence Testing

December 20, 2022

CausalDialogue: Modeling Utterance-level Causality in Conversations
Yi-Lin Tuan, Alon Albalak, Wenda Xu, Michael Saxon, Connor Pryor, Lise Getoor, William Yang Wang
Dialogue Generation Community Conversation Causal Direction Neural Conversational Causality Based Learning Conversational Framework

December 8, 2022

December 6, 2022

Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment
Weixiang Zhao, Yanyan Zhao, Zhuojun Li, Bing Qin
Causal Direction Causal Knowledge Causal Emotion Entailment Conversation Graph

December 5, 2022

This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy
Anuja Vats, Ahmed Mohammed, Marius Pedersen, Nirmalie Wiratunga
Line by Line Explanation Saliency Map Causal Direction Pre Change Information Causal Explanation Capsule Endoscopy Explanation Representation Format

December 2, 2022

Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow
Felipe Giori, Flavio Figueiredo
Causal Pattern Preliminary Study Causal Direction Causal Event Information Flow

Causal Direction

Papers

Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors

Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks

Reducing Causality to Functions with Structural Models

Bivariate Causal Discovery using Bayesian Model Selection

Active causal structure learning with advice

dotears: Scalable, consistent DAG estimation using observational and interventional data

The Magic of IF: Investigating Causal Reasoning Abilities in Large Language Models of Code

Discovering Causal Relations and Equations from Data

Causal Information Splitting: Engineering Proxy Features for Robustness to Distribution Shifts

Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach

Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model

Testing Causality for High Dimensional Data

Epsilon-Identifiability of Causal Quantities

Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing

CausalDialogue: Modeling Utterance-level Causality in Conversations

A probabilistic autoencoder for causal discovery

On Root Cause Localization and Anomaly Mitigation through Causal Inference

Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

This changes to that : Combining causal and non-causal explanations to generate disease progression in capsule endoscopy

Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow