Differentiable Multi-Target Causal Bayesian Experimental Design
- URL: http://arxiv.org/abs/2302.10607v2
- Date: Fri, 2 Jun 2023 11:16:33 GMT
- Title: Differentiable Multi-Target Causal Bayesian Experimental Design
- Authors: Yashas Annadani, Panagiotis Tigas, Desi R. Ivanova, Andrew Jesson,
Yarin Gal, Adam Foster, Stefan Bauer
- Abstract summary: We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting.
Existing methods rely on greedy approximations to construct a batch of experiments.
We propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs.
- Score: 43.76697029708785
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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