Learning Neural Causal Models with Active Interventions
- URL: http://arxiv.org/abs/2109.02429v1
- Date: Mon, 6 Sep 2021 13:10:37 GMT
- Title: Learning Neural Causal Models with Active Interventions
- Authors: Nino Scherrer, Olexa Bilaniuk, Yashas Annadani, Anirudh Goyal, Patrick
Schwab, Bernhard Sch\"olkopf, Michael C. Mozer, Yoshua Bengio, Stefan Bauer,
Nan Rosemary Ke
- Abstract summary: We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process.
Our method significantly reduces the required number of interactions compared with random intervention targeting.
We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
- Score: 83.44636110899742
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Discovering causal structures from data is a challenging inference problem of
fundamental importance in all areas of science. The appealing scaling
properties of neural networks have recently led to a surge of interest in
differentiable neural network-based methods for learning causal structures from
data. So far differentiable causal discovery has focused on static datasets of
observational or interventional origin. In this work, we introduce an active
intervention-targeting mechanism which enables a quick identification of the
underlying causal structure of the data-generating process. Our method
significantly reduces the required number of interactions compared with random
intervention targeting and is applicable for both discrete and continuous
optimization formulations of learning the underlying directed acyclic graph
(DAG) from data. We examine the proposed method across a wide range of settings
and demonstrate superior performance on multiple benchmarks from simulated to
real-world data.
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