Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
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2024-07
Publication Type
Conference Paper
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Abstract
Inferring causal structure from data is a challenging task of fundamental importance in science. Often, observational data alone is not enough to uniquely identify a system's causal structure. The use of interventional data can address this issue, however, acquiring these samples typically demands a considerable investment of time and physical or financial resources. In this work, we are concerned with the acquisition of interventional data in a targeted manner to minimize the number of required experiments. We propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention targeting function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.
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published
Book title
Advances in Neural Information Processing Systems 36
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50617 - 50647
Publisher
Curran
Event
37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023)
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Notes
Poster presented on December 13, 2023.
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Is new version of: https://openreview.net/forum?id=dmD63sv0TZ