Differentiable Causal Discovery

Differentiable causal discovery (DCD) uses continuous optimization to learn causal relationships represented as directed acyclic graphs (DAGs) from observational and interventional data. Current research focuses on improving the scalability and stability of DCD algorithms, addressing challenges like latent confounders and high-dimensionality, often employing neural network-based approaches and novel acyclicity constraints. These advancements enable more accurate and efficient causal inference in diverse fields, ranging from robotics and control systems to biology and social sciences, by providing robust methods for learning complex causal structures from large datasets.

Papers

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 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

June 15, 2024

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang
Causal Graph Speech Presence Extended Version Causal Learning Latent Confounders Human Skeleton Differentiable Causal Discovery

January 5, 2024

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
Daniel Waxman, Kurt Butler, Petar M. Djuric
Interpretable Way Differentiable Model Causal Strength Differentiable Causal Discovery

November 17, 2023

Stable Differentiable Causal Discovery
Achille Nazaret, Justin Hong, Elham Azizi, David Blei
Causal Relationship Differentiable Causal Discovery Sparse Causal

June 28, 2023

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Peide Huang, Xilun Zhang, Ziang Cao, Shiqi Liu, Mengdi Xu, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao
Real World Robot Trajectory Robot Simulation Dynamic Simulation Differentiable Causal Discovery Trajectory Alignment

March 6, 2023

Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting
An Zhang, Fangfu Liu, Wenchang Ma, Zhibo Cai, Xiang Wang, Tat-seng Chua
High Dimensional Causal Discovery Adaptive Weight Sample Reweighting Differentiable Causal Discovery Based Causal Discovery

November 24, 2022

Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
Mateusz Olko, Michał Zając, Aleksandra Nowak, Nino Scherrer, Yashas Annadani, Stefan Bauer, Łukasz Kuciński, Piotr Miłoś
Causal Discovery Causal Structure Appropriate Trust Gradient Estimator Early Intervention Differentiable Causal Discovery

June 15, 2022

Large-Scale Differentiable Causal Discovery of Factor Graphs
Romain Lopez, Jan-Christian Hütter, Jonathan K. Pritchard, Aviv Regev
Causal Discovery Causal Graph Factor Graph Differentiable Causal Discovery Sparse Causal

June 13, 2022

Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
Causal Relation Causal Representation Learning Differentiable Causal Discovery Interactive System Time Linkage Effect

June 9, 2022

On the Generalization and Adaption Performance of Causal Models
Nino Scherrer, Anirudh Goyal, Stefan Bauer, Yoshua Bengio, Nan Rosemary Ke
Strong Generalization Causal Structure Neural Causal Model Differentiable Causal Discovery AdaPtation Performance Better Out of Distribution Generalization

May 31, 2022

Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang, Yancheng Yuan, Xiangnan He, Tat-Seng Chua
Causal Discovery Causal Structure Differentiable Causal Discovery

March 4, 2022

Differentiable Causal Discovery Under Latent Interventions
Gonçalo R. A. Faria, André F. T. Martins, Mário A. T. Figueiredo
Variational Inference Causal Discovery Causal Graph Structural Causal Model Differentiable Causal Discovery Interventional Distribution

November 30, 2021

gCastle: A Python Toolbox for Causal Discovery
Keli Zhang, Shengyu Zhu, Marcus Kalander, Ignavier Ng, Junjian Ye, Zhitang Chen, Lujia Pan
Causal Discovery Causal Structure Graph Structure Learning Causal Structure Learning Python Toolbox Differentiable Causal Discovery