BaCaDI: Bayesian Causal Discovery with Unknown Interventions
- URL: http://arxiv.org/abs/2206.01665v1
- Date: Fri, 3 Jun 2022 16:25:48 GMT
- Title: BaCaDI: Bayesian Causal Discovery with Unknown Interventions
- Authors: Alexander H\"agele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath,
Bernhard Sch\"olkopf, Andreas Krause
- Abstract summary: BaCaDI operates in the continuous space of latent probabilistic representations of both causal structures and interventions.
In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.
- Score: 118.93754590721173
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Learning causal structures from observation and experimentation is a central
task in many domains. For example, in biology, recent advances allow us to
obtain single-cell expression data under multiple interventions such as drugs
or gene knockouts. However, a key challenge is that often the targets of the
interventions are uncertain or unknown. Thus, standard causal discovery methods
can no longer be used. To fill this gap, we propose a Bayesian framework
(BaCaDI) for discovering the causal structure that underlies data generated
under various unknown experimental/interventional conditions. BaCaDI is fully
differentiable and operates in the continuous space of latent probabilistic
representations of both causal structures and interventions. This enables us to
approximate complex posteriors via gradient-based variational inference and to
reason about the epistemic uncertainty in the predicted structure. In
experiments on synthetic causal discovery tasks and simulated gene-expression
data, BaCaDI outperforms related methods in identifying causal structures and
intervention targets. Finally, we demonstrate that, thanks to its rigorous
Bayesian approach, our method provides well-calibrated uncertainty estimates.
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