Experiment selection for causal discovery

By Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer. 2013

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Causal Reasoning from Meta-reinforcement Learning (2019, background): “We chose to focus on the RL approach because we are interested in agents that can learn about causes and effects not only from passive observations but also from active interactions with the environment (Hyttinen et al., 2013; Shanmugam et al., 2015).”
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training (2017, neutral): “earnacausalmodel,i.e.,thefunctionsandthedistributionsoftheexogenousvariables comprisingthestructuralequations8. Thereissigniﬁcantpriorworkonlearningcausalgraphsthat couldbeusedbeforeourmethod,seee.g. [11,17,18,16,35,24,12,32,21,20,19]. Whenthetrue causalgraphisunknownwecanuseanyfeasiblegraph,i.e.,an”
Neural Network Attributions: A Causal Perspective (2019, methodology): “We note that our work is different from a related subfield of structure learning (Eberhardt, 2007; Hoyer et al., 2009; Hyttinen et al., 2013; Kocaoglu et al., 2017), where the goal is to discern the causal structure in given data (for example, does feature A cause feature B or vice versa?).”
Identifying Best Interventions through Online Importance Sampling (2017, background): “There has been a lot of work (Mooij et al., 2016; Hyttinen et al., 2013; Eberhardt, 2008; Hauser & Bühlmann, 2012; Spirtes et al., 2001; Pearl, 2009a; Loh & Bühlmann, 2014; Shanmugam et al., 2015) on learning casual models from data and/or experiments and using it to estimate causal strength questio”
Learning linear cyclic causal models with latent variables (2012, background): “The optimal selection of experiments (given possible background knowledge) is closely related to the theory in combinatorics of finding so-called 'minimal completely separating systems' for directed graphs (see Hyttinen et al., 2012 and Spencer, 1970 for some relevant results).”
Learning Causal Graphs with Small Interventions (2015, background): “Furthermore [3] shows that the set of interventions to fully identify a causal DAG must satisfy a speciﬁc set of combinatorial conditions called a separating system 1 , when the intervention size is not constrained or is 1.”
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions (2019, background): “Related Work: Learning causal graphs from a combination of observational and interventional data has been studied in the literature [3, 11, 7, 20, 8, 12, 23].”