Related papers: Interventions, Where and How? Experimental Design for Causal Models at Scale

Interventions, Where and How? Experimental Design for Causal Models at Scale

URL: http://arxiv.org/abs/2203.02016v1
Date: Thu, 3 Mar 2022 20:59:04 GMT
Title: Interventions, Where and How? Experimental Design for Causal Models at Scale
Authors: Panagiotis Tigas, Yashas Annadani, Andrew Jesson, Bernhard Sch\"olkopf, Yarin Gal, Stefan Bauer
Abstract summary: Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability. In this paper, we incorporate recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework. We demonstrate the performance of the proposed method on synthetic graphs for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.
Score: 47.63842422086614
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability which introduces uncertainties in estimating the underlying structural causal model (SCM). Incorporating these uncertainties and selecting optimal experiments (interventions) to perform can help to identify the true SCM faster. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. In this paper, we incorporate recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, which allows for active causal discovery of nonlinear, large SCMs, while selecting both the target and the value to intervene with. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-R\`enyi, Scale Free) for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.

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