mcbo

The code companion to the paper "Model-based Causal Bayesian Optimization".

Credit

The starting point for the code in this repository was https://github.com/RaulAstudillo06/BOFN.

We build on BOTorch (https://botorch.org/).

Conda Environment

In a new conda environment with Python 3.9 run conda install botorch -c pytorch -c gpytorch -c conda-forge Then in the base directory of this repository: pip install -e .

On your system you now have a conda environment called "mcbo". This should be loaded whenever you run experiments.

Running

You can launch experiments by running scripts/runner.py and controlling the command line inputs. All experimental results are logged to the Weights and Bias service.

Naming

MCBO is the algorithm studied in the Model-based Causal Bayesian Optimization paper. The algorithm in this repo named MCBO is designed for just near-noiseless environments (like Function Networks). The algorithm named NMCBO implements MCBO for noisy environments.

File Structure

mcbo provides the core functionality of model-based causal bayesian optimization. In this folder, mcbo_trial.py implements the environment interaction loop. models/gp_network.py contains the class for fitting GPs for EIFN and MCBO models/eta_network.py contains the training loop for the custom optimizer used for optimizing the acquisition function in NMCBO. All other methods use default BOTorch optimizers.

scripts provides the key functionality for running experiments.