# Causal Bayesian Optimization

@article{Aglietti2020CausalBO, title={Causal Bayesian Optimization}, author={Virginia Aglietti and Xiaoyu Lu and Andrei Paleyes and Javier Gonz' alez}, journal={ArXiv}, year={2020}, volume={abs/2005.11741} }

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our approach combines ideas from causal inference, uncertainty quantification and sequential decision making. In particular, it generalizes Bayesian…

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## 13 Citations

Dynamic Causal Bayesian Optimization

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- 2021

This paper gives theoretical results detailing how one can transfer interventional information across time steps and defines a dynamic causal GP model which can be used to quantify uncertainty and find optimal interventions in practice.

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We introduce a new statistical causal inference method to estimate individual-level optimal causal intervention, that is, to which value we should set the value of a certain variable of an individual…

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- Computer ScienceNeurIPS
- 2020

This paper proposes the first multi-task causal Gaussian process (GP) model, which it is called DAG-GP, that allows for information sharing across continuous interventions and across experiments on different variables and achieves the best fitting performance in a variety of real and synthetic settings.

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- Computer ScienceNeurIPS
- 2021

Bayesian Interventional Mean Processes is introduced, a framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space, while taking into account the uncertainty within each causal graph.

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- Computer ScienceArXiv
- 2021

The Chronological Causal Bandit (CCB) is useful in discrete decision-making settings where the causal effects are changing across time and can be informed by earlier interventions in the same system.

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- Computer ScienceArXiv
- 2021

BoGraph is proposed, a SBO framework that learns the system structure from its logs and transforms it into a probabilistic graph model, which allows the optimizer to find efficient configurations faster than other methods.

Supplementary material for Dynamic Causal Bayesian Optimisation

- Computer Science
- 2021

Results over multiple datasets and replicates are presented, showing no evidence of convergence between stationary and non-stationary DAG and SCM variables, and independent manipulative variables are identified.

BoGraph: structured bayesian optimization from logs for expensive systems with many parameters

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- 2022

BoGraph is an auto-tuning framework that builds a graph of the system components before optimizing it using causal structure learning and contextualizes the system via decomposition of the parameter space for faster convergence and handling of many parameters.

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Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and to study electron-beam sensitive materials with high dose efficiency. In…

Multi-Objective Hyperparameter Optimization - An Overview

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- 2022

Overview FLORIAN KARL∗, Fraunhofer Institut für integrierte Schaltungen, Germany TOBIAS PIELOK∗, Ludwig-Maximilians-Universität München, Germany JULIA MOOSBAUER, Ludwig-Maximilians-Universität…

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