Observational Causal

Observational causal inference aims to uncover cause-and-effect relationships from data where controlled experiments are impossible or unethical, focusing on methods that reliably infer causal structures despite limitations in data. Current research emphasizes robust algorithms, such as score matching and those incorporating generative models or deep learning architectures like Transformers, to address challenges like confounding variables and non-stationarity in complex datasets, particularly in high-dimensional settings. These advancements are crucial for diverse fields, enabling more reliable causal analysis in areas like healthcare, autonomous systems, and sustainable agriculture where randomized controlled trials are often impractical.

Papers

October 20, 2023

Assumption violations in causal discovery and the robustness of score matching
Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti, Lorenzo Rosasco, Dominik Janzing, Bryon Aragam, Francesco Locatello
Native Robustness Causal Discovery Causal Structure Score Matching Modeling Assumption Causal Discovery Method Observational Causal

June 7, 2023

On the Use of Generative Models in Observational Causal Analysis
Nimrod Megiddo
Generative Model Greater Public Use Causal Relationship Causal Analysis Observational Data Probabilistic Generative Model Joint Distribution Observational Causal

June 6, 2023

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour
Rhys Howard, Lars Kunze
Causal Relationship Causal Discovery Method Causal Variable Observational Causal Multi Scene

May 21, 2023

Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Raw Data Causal Relation Causal Direction Observational Data Diverse Equation Observational Causal

February 23, 2023

Variable Importance Matching for Causal Inference
Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page
Causal Inference Treatment Effect Multiple Outcome Variable Importance Observational Causal Model Matching

January 31, 2023

Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Rhys Howard, Lars Kunze
Causal Discovery Method Temporal Pattern Autonomous Driving Domain Observational Causal Multi Scene

December 5, 2022

Observational and Interventional Causal Learning for Regret-Minimizing Control
Christian Reiser
External Control Causal Discovery Structural Causal Model Causal Representation Causal Constraint Observational Causal

November 6, 2022

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou, Alkiviadis Koukos, Dimitra Loka, Nikolaos Bartsotas, Charalampos Kontoes, Ioannis Athanasiadis
Causal Inference Digital Tool Digital Agriculture Observational Causal

April 2, 2022

Do learned representations respect causal relationships?
Lan Wang, Vishnu Naresh Boddeti
Meaningful Representation Causal Relationship Causal Relation Underlying Causal Observational Causal

February 7, 2022

Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records
Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, Kazem Rahimi
Causal Inference Causal Effect Causal Model Randomized Controlled Trial Observational Causal