Causal structure learning: A combinatorial perspective

C Squires, C Uhler - Foundations of Computational Mathematics, 2023 - Springer
In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

Linear causal disentanglement via interventions

C Squires, A Seigal, SS Bhate… - … Conference on Machine …, 2023 - proceedings.mlr.press
Causal disentanglement seeks a representation of data involving latent variables that are
related via a causal model. A representation is identifiable if both the latent model and the …

Causalgan: Learning causal implicit generative models with adversarial training

M Kocaoglu, C Snyder, AG Dimakis… - arXiv preprint arXiv …, 2017 - arxiv.org
We propose an adversarial training procedure for learning a causal implicit generative
model for a given causal graph. We show that adversarial training can be used to learn a …

Causal discovery in physical systems from videos

Y Li, A Torralba, A Anandkumar… - Advances in Neural …, 2020 - proceedings.neurips.cc
Causal discovery is at the core of human cognition. It enables us to reason about the
environment and make counterfactual predictions about unseen scenarios that can vastly …

Neural network attributions: A causal perspective

A Chattopadhyay, P Manupriya… - International …, 2019 - proceedings.mlr.press
We propose a new attribution method for neural networks developed using first principles of
causality (to the best of our knowledge, the first such). The neural network architecture is …

Causal bandits with unknown graph structure

Y Lu, A Meisami, A Tewari - Advances in Neural …, 2021 - proceedings.neurips.cc
In causal bandit problems the action set consists of interventions on variables of a causal
graph. Several researchers have recently studied such bandit problems and pointed out …

Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation

M Bermudez-Edo, P Barnaghi, K Moessner - Automation in Construction, 2018 - Elsevier
Abstract Smart Cities use different Internet of Things (IoT) data sources and rely on big data
analytics to obtain information or extract actionable knowledge crucial for urban planners for …

Causal inference in AI education: A primer

A Forney, S Mueller - Journal of Causal Inference, 2022 - degruyter.com
The study of causal inference has seen recent momentum in machine learning and artificial
intelligence (AI), particularly in the domains of transfer learning, reinforcement learning …

Regret analysis of bandit problems with causal background knowledge

Y Lu, A Meisami, A Tewari… - … on Uncertainty in Artificial …, 2020 - proceedings.mlr.press
We study how to learn optimal interventions sequentially given causal information
represented as a causal graph along with associated conditional distributions. Causal …

Learning causal graphs with small interventions

K Shanmugam, M Kocaoglu… - Advances in …, 2015 - proceedings.neurips.cc
We consider the problem of learning causal networks with interventions, when each
intervention is limited in size under Pearl's Structural Equation Model with independent …