Bayesdag: Gradient-based posterior inference for causal discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …
Bayesdag: Gradient-based posterior sampling for causal discovery
Bayesian causal discovery aims to infer the posterior distribution over causal models from
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …
observed data, quantifying epistemic uncertainty and benefiting downstream tasks …
Bayesian active causal discovery with multi-fidelity experiments
This paper studies the problem of active causal discovery when the experiments can be
done based on multi-fidelity oracles, where higher fidelity experiments are more precise and …
done based on multi-fidelity oracles, where higher fidelity experiments are more precise and …
Graph Agnostic Causal Bayesian Optimisation
We study the problem of globally optimising a target variable of an unknown causal graph
on which a sequence of soft or hard interventions can be performed. The problem of …
on which a sequence of soft or hard interventions can be performed. The problem of …
Experimental design for multi-channel imaging via task-driven feature selection
SB Blumberg, PJ Slator… - The Twelfth International …, 2024 - openreview.net
This paper presents a data-driven, task-specific paradigm for experimental design, to
shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices …
shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices …
Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning
Causal discovery with observational and interventional data plays an important role in
numerous fields. Due to the costly and potentially risky nature of intervention experiments …
numerous fields. Due to the costly and potentially risky nature of intervention experiments …
Amortized Active Causal Induction with Deep Reinforcement Learning
We present Causal Amortized Active Structure Learning (CAASL), an active intervention
design policy that can select interventions that are adaptive, real-time and that does not …
design policy that can select interventions that are adaptive, real-time and that does not …
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
Merging datasets across institutions is a lengthy and costly procedure, especially when it
involves private information. Data hosts may therefore want to prospectively gauge which …
involves private information. Data hosts may therefore want to prospectively gauge which …
A Meta-Learning Approach to Bayesian Causal Discovery
Discovering a unique causal structure is difficult due to both inherent identifiability issues,
and the consequences of finite data. As such, uncertainty over causal structures, such as …
and the consequences of finite data. As such, uncertainty over causal structures, such as …
Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
Representing uncertainty in causal discovery is a crucial component for experimental
design, and more broadly, for safe and reliable causal decision making. Bayesian Causal …
design, and more broadly, for safe and reliable causal decision making. Bayesian Causal …