Learning Causal Bayesian Network Structures From Experimental Data

By B. Ellis, Wing Hung Wong. 2008

207 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Causal Inference & Discovery

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Citations in the corpus

Bayesian Structure Learning with Generative Flow Networks (2022, methodology): “Working directly with DAGs leads to slow mixing though; to improve mixing, Friedman and Koller (2003) proposed a sampler in the space of node orders, that introduced a bias (Ellis and Wong, 2008).”
DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018, methodology): “Other approaches that avoid optimizing (4) directly include constraint-based methods (Spirtes and Glymour, 1991; Spirtes et al., 2000), hybrid methods (Tsamardinos et al., 2006; G´amez et al., 2011), and Bayesian methods (Ellis and Wong, 2008; Zhou, 2011; Niinim¨aki et al., 2016).”
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs (2013, background): “However, it has been reported that some experimental conditions affect products of inhibited proteins without changing their own phosphorylation state, and hence rather affect children of intervened variables in our framework rather than the intervention target itself (Ellis and Wong, 2008; Mooij an”
Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, methodology): “Statistical Methods Here we give a brief summary of four categories of GRN construction approaches; the detailed methodology for each approach has been described in other papers [11–13,17–19].”
Partition MCMC for Inference on Acyclic Digraphs (2015, methodology): “Attempts to remove the bias via importance sampling (Ellis and Wong, 2008) can help for small graphs, but they struggle as the size increases due to the exponential number of orders DAGs can be consistent with on average.”
DiBS: Differentiable Bayesian Structure Learning (2021, methodology): “Attempts to correct for its unintended structural bias are themselves NP-hard to compute and/or limit the parent size [38, 41].”
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021, methodology): “A popular choice for such an approach is the Markov Chain Monte Carlo (MCMC) with suitably chosen energy functions and heuristics [8, 26, 25, 22, 14, 9].”
BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA (2011, methodology): “This inferred network is comparable with that from Ellis and Wong (2008), which contains 9 true directed edges, 3 reversed, 8 missed, and 6 false edges.”
Causal network inference using biochemical kinetics (2014, unknown): “Network inference methods are typically rooted in linear or discrete models whose statistical and computational advantages facilitate exploration of large spaces of networks (e.g. Ellis and Wong, 2008; Maathuis et al ., 2009; Werhli et al. , 2006).”
Network Inference and Biological Dynamics (2011, methodology): “Otherwise, Markov chain Monte Carlo (MCMC) methods can be used to explore an eﬀectively smaller model space [Ellis and Wong (2008); Friedman and Koller (2003)].”
Structure Discovery in Bayesian Networks by Sampling Partial Orders (2016, methodology): “Our implementation of the Metropolis-coupled MCMC (MC3) method for the space of node orderings also serves as a proxy of a related implementation1 of Ellis and Wong (2008).”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “[260] 2020 score Granger – – no Bi Order-MCMC [60] 2003 sampling yes yes yes no DAG OG [53] 2008 sampling yes yes yes yes DAG EE-DAG [281] 2011 sampling yes yes yes yes DAG ZIPBN [35] 2020 sampling yes no yes no DAG LiNGAM [221] 2006 asymmetries yes no yes no DAG LV LiNGAM [101] 2008 asymmetries no ”