Active Causal

Active causal learning focuses on efficiently discovering causal relationships by strategically selecting data points or interventions for observation or experimentation. Current research emphasizes developing amortized methods, often employing neural networks (like transformers) and reinforcement learning, to rapidly and adaptively select informative data, even generalizing to unseen scenarios. This approach is particularly valuable in high-dimensional settings, such as molecular design and personalized medicine, where exhaustive experimentation is impractical, enabling more efficient causal inference and improved decision-making in various scientific and engineering domains. Bayesian methods are also prominent, providing principled ways to quantify uncertainty and guide data acquisition.

Papers

October 28, 2024

Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots
Pablo de los Riscos, Fernando Corbacho
Latent Variable Autonomous Robot Causal Model Structural Causal Model Temporal Causal Active Causal

July 25, 2024

Amortized Active Learning for Nonparametric Functions
Cen-You Li, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer
Active Learning Nonparametric Regression Active Causal Functional Prior Active SEt

May 26, 2024

Amortized Active Causal Induction with Deep Reinforcement Learning
Yashas Annadani, Panagiotis Tigas, Stefan Bauer, Adam Foster
Deep Reinforcement Learning Intervention Policy Active Causal Node Intervention

April 5, 2024

Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions
Zachary R. Fox, Ayana Ghosh
Molecular Representation Chemical Space Quantum Chemistry Molecule Task Active Causal

May 25, 2023

Passive learning of active causal strategies in agents and language models
Andrew Kyle Lampinen, Stephanie C Y Chan, Ishita Dasgupta, Andrew J Nam, Jane X Wang
Language Model Pre Trained Language Model Agent Smith Causal Intervention Passive Learning Active Causal

September 10, 2022

Active Learning for Optimal Intervention Design in Causal Models
Jiaqi Zhang, Louis Cammarata, Chandler Squires, Themistoklis P. Sapsis, Caroline Uhler
Active Learning Causal Model Structural Causal Model Sequential Experimental Design Optimal Intervention Active Causal

June 20, 2022

Actively learning to learn causal relationships
Chentian Jiang, Christopher G. Lucas
Active Learning Causal Relationship Active Causal Causal Overhypotheses

June 4, 2022

Active Bayesian Causal Inference
Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen
Causal Discovery Causal Model Active Causal

March 3, 2022

Interventions, Where and How? Experimental Design for Causal Models at Scale
Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Schölkopf, Yarin Gal, Stefan Bauer
Causal Discovery Visual Analogue Scale Structural Causal Model Experimental Design Health Intervention Bayesian Causal Discovery Active Causal

November 3, 2021

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
Synthetic Data Treatment Effect Multiple Outcome Observational Data Deep Active Learning Acquisition Function Active Causal