Causal Relationship

Causal relationship research aims to understand and model cause-and-effect relationships within complex systems, moving beyond simple correlations. Current research focuses on developing algorithms and models, such as Bayesian networks, that can effectively learn causal structures from observational and interventional data, often incorporating expert knowledge or handling high-dimensional and incomplete datasets. These advancements are crucial for improving decision-making in various fields, including healthcare, finance, and robotics, by enabling more accurate predictions and interventions based on a deeper understanding of underlying causal mechanisms. The development of robust benchmarking frameworks further enhances the reliability and reproducibility of causal discovery methods.

Papers

December 3, 2022

Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure
Liye Jia, Fengyufan Yang, Ka Lok Man, Erick Purwanto, Sheng-Uei Guan, Jeremy Smith, Yutao Yue
High Uncertainty Anticipation Causal Structure Causal Relationship Actual Cause Information Theoretic Framework Macroscopic Model

November 29, 2022

Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test
Chih-Yuan Chiu, Kshitij Kulkarni, Shankar Sastry
Causal Discovery Causal Relationship Causal Pattern Conditional Independence Rare Event Linear Time Invariant System

November 3, 2022

A Consistent Estimator for Confounding Strength
Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar, Ulrike von Luxburg
Causal Relationship Estimated Team Strength Random Matrix Consistent Estimator

October 30, 2022

Nonlinear Causal Discovery via Kernel Anchor Regression
Wenqi Shi, Wenkai Xu
Causal Relationship Anchor Graph Structural Equation

October 28, 2022

Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning
Harshad Khadilkar, Hardik Meisheri
Reinforcement Learning Causal Relationship Policy Reinforcement Learning State Action Pair Contrastive Data Proxy Based Contrastive Replay

October 27, 2022

Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
Utkarsh Soni, Nupur Thakur, Sarath Sreedharan, Lin Guan, Mudit Verma, Matthew Marquez, Subbarao Kambhampati
Agent Smith Preference Feedback Causal Relationship Knowledge Acquisition Vocabulary Expansion Preference Specification

October 26, 2022

Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski
Causal Relationship Causal Discovery Method Causal Categorization Historical Robot Rhino

October 13, 2022

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Studies
Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu
Causal Relationship Granger Causality Longitudinal Study Longitudinal Dataset Causality Based Learning

September 23, 2022

Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
Violeta Teodora Trifunov, Maha Shadaydeh, Joachim Denzler
Variational Autoencoder Causal Relationship Causal Effect Estimation Latent Confounding Knockoff Statistic Aerosol Cloud

September 7, 2022

Causal discovery for time series with latent confounders
Christian Reiser
Time Series Causal Discovery Causal Relationship Observational Data Latent Confounders

August 17, 2022

August 15, 2022

Anticipating Performativity by Predicting from Predictions
Celestine Mendler-Dünner, Frances Ding, Yixin Wang
Human Prediction Causal Effect Causal Relationship Causal Inference Task Performative Power Causal Identifiability

August 2, 2022

CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
Giovanni Menegozzo, Diego Dall'Alba, Paolo Fiorini
Causal Relationship Fault Detection Manufacturing Process Data Driven Causal

July 16, 2022

Multiscale Causal Structure Learning
Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
Causal Structure Causal Relationship

July 7, 2022

CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships
Rebecca Roelofs, Liting Sun, Ben Caine, Khaled S. Refaat, Ben Sapp, Scott Ettinger, Wei Chai
Causal Relationship Causal Direction Motion Forecasting Waymo Open Motion Dataset Robustness Benchmark

June 20, 2022

Actively learning to learn causal relationships
Chentian Jiang, Christopher G. Lucas
Active Learning Causal Relationship Active Causal Causal Overhypotheses

June 16, 2022

Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik
New Machine Causal Inference Causal Relationship Causal Overhypotheses

June 3, 2022

Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength
Jiageng Zhu, Hanchen Xie, Wael AbdAlmageed
Causal Relationship High Dimensional Data Causal Representation Learning Causal Direction Causal Representation Partial Supervision

May 27, 2022

Fast Causal Orientation Learning in Directed Acyclic Graphs
Ramin Safaeian, Saber Salehkaleybar, Mahmoud Tabandeh
Causal Relationship Directed Acyclic Graph Causal DAG Causal Discovery Task