1 citations · 2 across the 3 of their papers we have counts for
9 papers
Causal Climate Emulation with Bayesian Filtering
Sebastian Hickman, Ilija Trajkovic, Julia Kaltenborn +6
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These simulations are highly computationally e…
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Raanan Y. Rohekar, Yaniv Gurwicz, Sungduk Yu +2
Are generative pre-trained transformer (GPT) models, trained only to predict the next token, implicitly learning a world model from which sequences are generated one token at a tim…
Causal Representation Learning in Temporal Data via Single-Parent Decoding
Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn +6
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Niño…
CLEAR: Causal Explanations from Attention in Neural Recommenders
Shami Nisimov, Raanan Y. Rohekar, Yaniv Gurwicz +2
We present CLEAR, a method for learning session-specific causal graphs, in the possible presence of latent confounders, from attention in pre-trained attention-based recommenders.…
Improving Efficiency and Accuracy of Causal Discovery Using a Hierarchical Wrapper
Shami Nisimov, Yaniv Gurwicz, Raanan Y. Rohekar +1
Causal discovery from observational data is an important tool in many branches of science. Under certain assumptions it allows scientists to explain phenomena, predict, and make de…
A Single Iterative Step for Anytime Causal Discovery
Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov +1
We present a sound and complete algorithm for recovering causal graphs from observed, non-interventional data, in the possible presence of latent confounders and selection bias. We…