Causal Discovery

Causal discovery aims to infer cause-and-effect relationships from data, moving beyond simple correlations to understand underlying mechanisms. Current research emphasizes developing algorithms and models, including constraint-based methods, score-matching techniques, and those leveraging neural networks (like graph neural networks and normalizing flows), to efficiently handle high-dimensional data, time series, and latent variables, often incorporating expert knowledge or interventional data to improve accuracy. This field is crucial for advancing scientific understanding across diverse domains, from biology and healthcare to climate science and robotics, by enabling more accurate modeling, prediction, and intervention design. Improved causal discovery methods are leading to more reliable insights and more effective decision-making in complex systems.

Papers

November 8, 2024

Cross-validating causal discovery via Leave-One-Variable-Out
Daniela Schkoda, Philipp Faller, Patrick Blöbaum, Dominik Janzing
Causal Discovery Causal Model Causal Discovery Algorithm Leave One Out Cross Validation

November 6, 2024

Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu
Causal Discovery Causal Graph Bandit Algorithm Causal Bandit Structure Discovery Regret Learning

November 4, 2024

Causal Discovery and Classification Using Lempel-Ziv Complexity
Dhruthi, Nithin Nagaraj, Harikrishnan N B
Classification Code Causal Discovery Causality Analysis Visual Causality Discovery

November 2, 2024

Marginal Causal Flows for Validation and Inference
Daniel de Vassimon Manela, Laura Battaglia, Robin J. Evans
Scientific Inference Causal Discovery Causal Effect External Validation Causal View Unmeasured Confounding Graphical Normalizing Flow

October 30, 2024

October 28, 2024

LLM-initialized Differentiable Causal Discovery
Shiv Kampani, David Hidary, Constantijn van der Poel, Martin Ganahl, Brenda Miao
Causal Discovery Causal Graph Causal Discovery Method Differentiable Causal Discovery

October 26, 2024

Sample Efficient Bayesian Learning of Causal Graphs from Interventions
Zihan Zhou, Muhammad Qasim Elahi, Murat Kocaoglu
Causal Discovery Causal Graph Sample Efficient Health Intervention

October 25, 2024

Robust Time Series Causal Discovery for Agent-Based Model Validation
Gene Yu, Ce Guo, Wayne Luk
Causal Discovery Agent Based Model Causal Discovery Method Temporal Causal Discovery

October 24, 2024

October 22, 2024

MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming
Abdelmonem Elrefaey, Rong Pan
Causal Discovery Bayesian Network Programming Assistance Integer Programming Integer Arithmetic Markov Equivalence Class Efficient Discovery

October 21, 2024

LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation
Tejumade Afonja, Ivaxi Sheth, Ruta Binkyte, Waqar Hanif, Thomas Ulas, Matthias Becker, Mario Fritz
Large Language Model Global Evaluation Causal Discovery Synthetic Data Generation Regulatory Network Single Cell RNA scRNA Seq

October 20, 2024

Structural Causality-based Generalizable Concept Discovery Models
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
Variational Autoencoder Causal Discovery Inner Structure Concept Discovery Generative Factor

October 15, 2024

LoSAM: Local Search in Additive Noise Models with Unmeasured Confounders, a Top-Down Global Discovery Approach
Sujai Hiremath, Kyra Gan, Promit Ghosal
Causal Discovery Causal Structure Local Search Text Based Confounders Structural Equation Additive Noise Automatic Discovery

October 9, 2024

Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick
Causal Discovery Causal Structure Causal Model Causal Representation Learning Temporal Data Latent Causal

October 8, 2024

October 4, 2024

Predicting perturbation targets with causal differential networks
Menghua Wu, Umesh Padia, Sean H. Murphy, Regina Barzilay, Tommi Jaakkola
Causal Discovery Causal Graph Causal Discovery Algorithm Causal Approach Causal Network Targeted Perturbation

October 3, 2024

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series
Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Causal Discovery Causal Structure Causal Model Causal Relationship Causal Analysis K Content Interventional Data