Interventions, Where and How? Experimental Design for Causal Models at Scale
Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. 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. This work incorporates recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, allowing for active causal discovery of large, nonlinear SCMs while selecting both the interventional target and the value. 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 \emph{in-silico} single-cell gene regulatory network dataset, DREAM.
Code (1)
Tasks
Causal DiscoveryExperimental DesignSimilar Papers 제목 키워드 기반
Experimental Design for Learning Causal Graphs with Latent Variables
We consider the problem of learning causal structures with latent variables using interventions. Our objective is not only to learn the causal graph between the observed variables, but to locate unobserved variables that…
Experimental DesignActive Learning for Optimal Intervention Design in Causal Models
Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When the space of possible interventions is la…
Active LearningExperimental DesignDifferentiable Multi-Target Causal Bayesian Experimental Design
We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions c…
Causal DiscoveryExperimental DesignRegret Analysis of Bandit Problems with Causal Background Knowledge
We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like onlin…
Thompson SamplingNear-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Causal structure learning is a key problem in many domains. Causal structures can be learnt by performing experiments on the system of interest. We address the largely unexplored problem of designing a batch of experimen…
Experimental DesignInformativeness