@inproceedings{annadani2023differentiable,
author = {Annadani, Yashas and Tigas, Panagiotis and Ivanova, Desi R. and Jesson, Andrew and Gal, Yarin and Foster, Adam and Bauer, Stefan},
title = {Differentiable Multi-Target Causal Bayesian Experimental Design},
booktitle = {ICML 2023},
year = {2023},
month = {June},
abstract = {We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.},
url = {http://approjects.co.za/?big=en-us/research/publication/differentiable-multi-target-causal-bayesian-experimental-design/},
}