Causal structure learning: A combinatorial perspective
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 …
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …
Generalizing goal-conditioned reinforcement learning with variational causal reasoning
As a pivotal component to attaining generalizable solutions in human intelligence,
reasoning provides great potential for reinforcement learning (RL) agents' generalization …
reasoning provides great potential for reinforcement learning (RL) agents' generalization …
Trustworthy reinforcement learning against intrinsic vulnerabilities: Robustness, safety, and generalizability
A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …
Causal bandits with unknown graph structure
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 …
graph. Several researchers have recently studied such bandit problems and pointed out …
Differentiable multi-target causal bayesian experimental design
We introduce a gradient-based approach for the problem of Bayesian optimal experimental
design to learn causal models in a batch setting—a critical component for causal discovery …
design to learn causal models in a batch setting—a critical component for causal discovery …
Budgeted and non-budgeted causal bandits
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed
bandit problem with side-information. First, we study this problem when interventions are …
bandit problem with side-information. First, we study this problem when interventions are …
Active structure learning of causal DAGs via directed clique trees
C Squires, S Magliacane… - Advances in …, 2020 - proceedings.neurips.cc
A growing body of work has begun to study intervention design for efficient structure learning
of causal directed acyclic graphs (DAGs). A typical setting is a\emph {causally sufficient} …
of causal directed acyclic graphs (DAGs). A typical setting is a\emph {causally sufficient} …
Verification and search algorithms for causal DAGs
D Choo, K Shiragur… - Advances in Neural …, 2022 - proceedings.neurips.cc
We study two problems related to recovering causal graphs from interventional data:(i)
$\textit {verification} $, where the task is to check if a purported causal graph is correct, and …
$\textit {verification} $, where the task is to check if a purported causal graph is correct, and …
Sample efficient active learning of causal trees
K Greenewald, D Katz, K Shanmugam… - Advances in …, 2019 - proceedings.neurips.cc
We consider the problem of experimental design for learning causal graphs that have a tree
structure. We propose an adaptive framework that determines the next intervention based on …
structure. We propose an adaptive framework that determines the next intervention based on …
Adaptively exploiting d-separators with causal bandits
Multi-armed bandit problems provide a framework to identify the optimal intervention over a
sequence of repeated experiments. Without additional assumptions, minimax optimal …
sequence of repeated experiments. Without additional assumptions, minimax optimal …