Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Multi-Objective Causal Bayesian Optimization
Authors: Shriya Bhatija, Paul-David Zuercher, Jakob Thumm, Thomas Bohnรฉ
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world causal graphs demonstrate the superiority of our approach over non-causal multi-objective Bayesian optimization in settings where causal information is available. |
| Researcher Affiliation | Academia | 1Department of Computer Engineering, Technical University of Munich, Munich, Germany 2Department of Engineering, University of Cambridge, Cambridge, United Kingdom 3The Alan Turing Institute, London, United Kingdom. Correspondence to: Shriya Bhatija <EMAIL>. |
| Pseudocode | Yes | We propose our algorithm to solve MO-CBO problems1, for which the procedure is summarized in Algorithm 1. It assumes a known causal graph G, Y, X, C , prior data D, and a set S {OG,Y, MG,Y, P(X)} that specifies which local problems to consider. |
| Open Source Code | Yes | 1The full implementation of our algorithm is available at https://github.com/Shriya Bhatija/MO-CBO |
| Open Datasets | Yes | The model is inspired by the German Credit UCI dataset (Murphy, 1994), with causal dependencies adapted from Karimi et al. (2020). ... Murphy, P. M. UCI repository of machine learning databases, 1994. URL ftp://ftp.ics.uci.edu/ pub/machine-learning-databases/. ... This model originates from previous works of Ferro et al. (2015), and is based on real-world causal relationships. |
| Dataset Splits | No | We assume to have an initial dataset D = {((Xs, xk s), ยต(Xs, xk s))}K,|S| k=1,s=1 with K = 5 samples per intervention set. |
| Hardware Specification | Yes | All experiments were executed on a machine equipped with an Apple M2 processor and 8GB of RAM. |
| Software Dependencies | No | We implement q NEHVI using the botorch library. |
| Experiment Setup | Yes | The batch size is set to 5. For reproducibility, all experiments are run across 10 random seeds, resulting in varying initializations of D. ... Par EGO: ...ฯ = 0.5 as initial standard deviation. ... TSEMO: ...use 100 points for spectral sampling. ... q NEHVI: ...use 10 optimization restarts, and 64 raw samples for acquisition maximization. Moreover, the acquisition function uses a Sobol QMC sampler with 128 samples. |