Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
CoRR(2022)
摘要
Inferring causal structure from data is a challenging task of fundamental
importance in science. Observational data are often insufficient to identify a
system's causal structure uniquely. While conducting interventions (i.e.,
experiments) can improve the identifiability, such samples are usually
challenging and expensive to obtain. Hence, experimental design approaches for
causal discovery aim to minimize the number of interventions by estimating the
most informative intervention target. In this work, 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 acquisition 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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