Causal Ordering

Causal ordering, the determination of the sequence of cause-and-effect relationships within a system, is a central problem in causal inference. Current research focuses on developing algorithms and model architectures, such as normalizing flows and additive structural equation models, that can robustly infer causal orderings from observational and interventional data, even in the presence of latent variables and high dimensionality. These advancements improve the accuracy and efficiency of causal discovery, impacting fields like genetics, economics, and robotics by enabling more reliable causal analysis and prediction. Furthermore, research is actively addressing challenges like handling heteroscedasticity and standardizing datasets to improve the generalizability of causal discovery methods.

Papers

October 30, 2024

QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
Mohammad Shahverdikondori, Ehsan Mokhtarian, Negar Kiyavash
Causal Discovery Causal Graph Causal Ordering Linear Non Gaussian Acyclic Model

October 11, 2024

Efficient Differentiable Discovery of Causal Order
Mathieu Chevalley, Arash Mehrjou, Patrick Schwab
Auxiliary Sorting Network Rearranges Causal Discovery Algorithm Causal Order Causal Ordering Differentiable Causal Discovery

July 6, 2024

Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows
Nu Hoang, Bao Duong, Thin Nguyen
Causal Discovery Normalizing Flow Nonlinear Model Causal Ordering

June 17, 2024

Standardizing Structural Causal Models
Weronika Ormaniec, Scott Sussex, Lars Lorch, Bernhard Schölkopf, Andreas Krause
Causal Inference Causal Structure Structural Causal Model Causal Ordering

May 28, 2024

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
Mathieu Chevalley, Patrick Schwab, Arash Mehrjou
Practical Algorithm Causal Inference Causal Structure Learning Formal Guarantee Causal Order Causal Ordering Interventional Data

March 24, 2024

Learning Directed Acyclic Graphs from Partial Orderings
Ali Shojaie, Wenyu Chen
Causal Relationship Directed Acyclic Graph Causal Ordering Estimation Framework Partial Order

March 6, 2024

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Victor Akinwande, J. Zico Kolter
Causal Discovery Single GPU Speed Effect Causal Discovery Method Large Scale Datasets Gene Expression Data Causal Ordering Causal DAG

February 22, 2024

Rao-Blackwellising Bayesian Causal Inference
Christian Toth, Christian Knoll, Franz Pernkopf, Robert Peharz
Causal Structure Causal Order Causal Ordering

February 13, 2024

Adjustment Identification Distance: A gadjid for Causal Structure Learning
Leonard Henckel, Theo Würtzen, Sebastian Weichwald
Causal Discovery Causal Structure Learning Causal Discovery Algorithm Causal Feature Causal Ordering

January 12, 2024

Boosting Causal Additive Models
Maximilian Kertel, Nadja Klein
Structural Equation Boosting Algorithm Causal Order Causal Ordering

August 13, 2023

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
Feng Xie, Biwei Huang, Zhengming Chen, Ruichu Cai, Clark Glymour, Zhi Geng, Kun Zhang
Latent Variable Causal Structure Latent Causal Causal Ordering Linear Non Gaussian Acyclic Model Dependent Noise

July 16, 2023

Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Causal Ordering Causal DAG Temporal Causal

July 11, 2023

A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori, Pedro Sanchez, Konstantinos Vilouras, Ben Glocker, Sotirios A. Tsaftaris
Variational Inference Causal Discovery Latent Variable Causal Representation Learning Unsupervised Representation Learning Causal Ordering

June 13, 2023

On Guiding Search in HTN Temporal Planning with non Temporal Heuristics
Nicolas Cavrel, Damien Pellier, Humbert Fiorino
Search Query Task Decomposition Temporal Planning Causal Ordering Hierarchical Task Network Hierarchical Task Network Planning

June 8, 2023

Causal normalizing flows: from theory to practice
Adrián Javaloy, Pablo Sánchez-Martín, Isabel Valera
Theoretical Understanding Causal Graph Causal Event General Flow Causal Ordering Generating Process

October 30, 2022

Searching for Deviations in Trading Systems: Combining Control-Flow and Data Perspectives
Julio C. Carrasquel, Irina A. Lomazova
Data Perspective Petri Net Subject Level Deviation Causal Ordering Trading System

June 25, 2022

Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and Effect Classification
Carlos Fernández-Loría, Jorge Loría
New Framework Causal Effect Estimation Causal Ordering Effect Prediction Effect Estimation Causal Score

November 4, 2021

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning
Sindre Benjamin Remman, Inga Strümke, Anastasios M. Lekkas
Deep Reinforcement Learning Explainable AI Domain Knowledge Shapley Value Causal Relation Causal Event Causal Ordering