Ground Truth Causal

Ground truth causal discovery aims to identify the true causal relationships between variables in data, a crucial step for reliable causal inference and informed decision-making. Current research focuses on developing more efficient algorithms, including constraint-based methods that reduce computational complexity and those leveraging symbolic reasoning or large language models for causal graph extraction from data or text. These advancements are improving the accuracy and scalability of causal discovery, particularly in complex domains with high-dimensional data or limited interventional capabilities, leading to more robust causal analyses across diverse scientific fields and practical applications. The development of realistic synthetic datasets and open-source libraries further facilitates benchmarking and wider adoption of these methods.

Papers

June 3, 2024

Causal Discovery with Fewer Conditional Independence Tests
Kirankumar Shiragur, Jiaqi Zhang, Caroline Uhler
Causal Discovery Causal Graph Conditional Independence Ground Truth Causal Constraint Based Causal Discovery

May 18, 2024

Argumentative Causal Discovery
Fabrizio Russo, Anna Rapberger, Francesca Toni
Causal Inference Causal Discovery Causal Relationship Assumption Based Argumentation Ground Truth Causal

December 22, 2023

Zero-shot Causal Graph Extrapolation from Text via LLMs
Alessandro Antonucci, Gregorio Piqué, Marco Zaffalon
Large Language Model Text Modality Causal Inference Causal Graph Causal Relation Causal View Ground Truth Causal Zero Shot Causal

November 5, 2023

Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge
Causal Representation Learning Latent Causal Ground Truth Causal Node Intervention

October 3, 2023

CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
Yuxiao Cheng, Ziqian Wang, Tingxiong Xiao, Qin Zhong, Jinli Suo, Kunlun He
Time Series Causal Discovery Causal Graph Benchmark Platform Causal Transformer Ground Truth Causal

August 16, 2023

Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
Yuewei Yang, Hai Li, Yiran Chen
Representation Learning Real World Causal Inference Self Supervised Visual Representation Ground Truth Causal

July 18, 2023

Self-Compatibility: Evaluating Causal Discovery without Ground Truth
Philipp M. Faller, Leena Chennuru Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing
Ground Truth Causal Graph Causal Learning Causal Discovery Algorithm Self Compatibility Ground Truth Causal

June 19, 2023

$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Steffen Sonntag, Martin Roth
Causal Discovery Causal Structure Data Generation Causal Discovery Method Ground Truth Causal True Causal

June 9, 2023

Adaptivity Complexity for Causal Graph Discovery
Davin Choo, Kirankumar Shiragur
Causal Discovery Causal Graph Adaptive Algorithm Adaptive Model Ground Truth Causal Causal Graph Discovery

January 25, 2023

Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum, Weiran Yao, Chenghao Liu, Wenzhuo Yang, Paul Josel, Shelby Heinecke, Eric Hu, Huan Wang, Stephen Hoi, Caiming Xiong, Kun Zhang, Juan Carlos Niebles
Time Series Causal Inference Causal Discovery Tabular Data Causal Analysis Causal Machine Ground Truth Causal

January 9, 2023

Subset verification and search algorithms for causal DAGs
Davin Choo, Kirankumar Shiragur
Causal Inference Causal Graph Directed Acyclic Graph Formal Verification Search Algorithm Ground Truth Causal

February 4, 2022

Deep End-to-end Causal Inference
Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang
Causal Inference Causal Discovery Ground Truth Causal