Active Learning of Causal Networks with Intervention Experiments and Optimal Designs

By Yangbo He, Zhi Geng. 2008

205 citations recorded by the source corpus.

How researchers see this paper

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

Citations in the corpus

Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “For example, two diﬀerent interventional data samples, or a combination of observational and interventional data [He and Geng., 2008, cf.] belong to such a scenario.”
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs (2011, methodology): “He and Geng (2008) present a greedy procedure in which interventional data is uniquely used for deciding about edge orientations; this is not favorable from a statistical point of view since interventional data can also help to improve the estimation of the skeleton (or, more generally, the…”
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs (2013, methodology): “He and Geng (2008) present a method which ﬁrst estimates the observational Markov equivalence class and then in a second step, it identiﬁes additional structure using interventional data.”
Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets (2016, background): “In general, interventions with known targets are informative to identify the causal network in a Markov equivalence class (Eberhardt et al., 2005; He and Geng, 2008).”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, unknown): “PC [223] 1993 constraint yes yes yes no CPDAG CCD [196] 1996 constraint yes yes both no PAG FCI [223] 2000 constraint no yes yes no PAG TPDA [31] 2002 constraint yes yes yes no CPDAG CPC [190] 2006 constraint yes relaxed yes no CPDAG KCL [227] 2007 constraint yes yes yes no CPDAG ION [234] 2008 cons”
Intrinsically motivated information foraging (2010, methodology): “The problem of choosing a design of experiments in order to maximize some overall utility has been treated extensively in the statistics community [4], [5], [6] under the terminology of Bayesian optimal experimental design (OED), and the same basic idea has been applied in many science and engineeri”
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (2020, background): “vention, which enables agents to explore possible directions of undirected causal edges [153, 154].”
Experiment selection for causal discovery (2013, background): “…in combination with novel inference principles, play an increasingly prominent role (Cooper and Yoo, 1999; Tong and Koller, 2001; Murphy, 2001; Eaton and Murphy, 2007; Meganck et al., 2005; Eberhardt et al., 2005; He and Geng, 2008; Hyttinen et al., 2010, 2011; Claassen and Heskes, 2010).”
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks (2019, background): “A different line of work approaches the experimental design problem from a theoretical perspective on the graph-level [26–30].”
Learning Bayesian networks: approaches and issues (2011, background): “Other interesting papers on the subject include those by Heckerman and Breese (1996), Boutilier et al.”