# 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…

## Figures from this paper

## 14 Citations

### Dynamic Causal Bayesian Optimization

- Computer ScienceNeurIPS
- 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.

### Estimating individual-level optimal causal interventions combining causal models and machine learning models

- Computer Science
- 2021

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…

### Multi-task Causal Learning with Gaussian Processes

- 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.

### BayesIMP: Uncertainty Quantification for Causal Data Fusion

- 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.

### Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation

- Computer Science
- 2022

This paper proposes that DCBO can act as a blue agent when provided with a view of a simulated network and a causal model of how a red agent spreads within that network and provides numerical results which lay the foundations for future work in this space.

### Chronological Causal Bandits

- 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.

### BoGraph: Structured Bayesian Optimization From Logs for Systems with High-dimensional Parameter Space

- 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

- Computer ScienceEuroMLSys@EuroSys
- 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.

### Automatic parameter selection for electron ptychography via Bayesian optimization

- PhysicsScientific reports
- 2022

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…

## References

SHOWING 1-10 OF 24 REFERENCES

### Structural Causal Bandits: Where to Intervene?

- Computer ScienceNeurIPS
- 2018

This paper builds a new algorithm that takes as input a causal structure and finds a minimal, sound, and complete set of qualified arms that an agent should play to maximize its expected reward and empirically demonstrates that the new strategy learns an optimal policy and leads to orders of magnitude faster convergence rates when compared with its causal-insensitive counterparts.

### Generalized Thompson sampling for sequential decision-making and causal inference

- Computer ScienceComplex Adapt. Syst. Model.
- 2014

The results suggest that Thompson sampling might not merely be a useful heuristic, but a principled method to address problems of adaptive sequential decision-making and causal inference.

### Causal Bandits: Learning Good Interventions via Causal Inference

- Computer ScienceNIPS
- 2016

A new algorithm is proposed that exploits the causal feedback and proves a bound on its simple regret that is strictly better (in all quantities) than algorithms that do not use the additional causal information.

### Review of Causal Discovery Methods Based on Graphical Models

- Computer ScienceFront. Genet.
- 2019

This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.

### Bandits with Unobserved Confounders: A Causal Approach

- Computer ScienceNIPS
- 2015

It is shown that to achieve low regret in certain realistic classes of bandit problems (namely, in the face of unobserved confounders), both experimental and observational quantities are required by the rational agent.

### Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

- Computer ScienceICLR
- 2019

The Counterfactually-Guided Policy Search (CF-GPS) algorithm is proposed, which leverages structural causal models for counterfactual evaluation of arbitrary policies on individual off-policy episodes and can improve on vanilla model-based RL algorithms by making use of available logged data to de-bias model predictions.

### Bayesian Optimization in a Billion Dimensions via Random Embeddings

- Computer ScienceJ. Artif. Intell. Res.
- 2016

Empirical results confirm that REMBO can effectively solve problems with billions of dimensions, provided the intrinsic dimensionality is low, and show thatREMBO achieves state-of-the-art performance in optimizing the 47 discrete parameters of a popular mixed integer linear programming solver.

### Deconfounding Reinforcement Learning in Observational Settings

- Computer ScienceArXiv
- 2018

This work considers the problem of learning good policies solely from historical data in which unobserved factors affect both observed actions and rewards, and for the first time that confounders are taken into consideration for addressing full RL problems with observational data.

### Counterfactual Multi-Agent Policy Gradients

- Computer ScienceAAAI
- 2018

A new multi-agent actor-critic method called counterfactual multi- agent (COMA) policy gradients that uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.

### Causal diagrams for empirical research

- Philosophy
- 1995

SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper…