A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

By Gregory F. Cooper. 1997

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, methodology): “This simple procedure for identifying chains W → X → Y is useful in exploratory data analysis (Chen et al., 2007; Cooper, 1997), where a large number of possible causal relations X → Y are unquantified but can be screened using observational data before experiments are performed.”
Learning Bayesian networks: approaches and issues (2011, methodology): “Another non-parametric way of estimating the conditional densities is given by Monti and Cooper (1997a), who use neural networks in this regard.”
Joint Causal Inference from Multiple Contexts (2016, methodology): “Finally, a single conditional independence in the pooled data (as in LCD) provides the desired evidence for an unconfounded causal relation between two gene expression levels.”
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example (2014, methodology): “Cooper [1] proved that: Theorem 1.1.”
Advances in variable selection methods I: Causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships (2012, background): “Aggregation of the top-four selected variables by each method gave three true variables by FOU, two by HITON-MB and LCD2, and none by all other methods.”
Causal Transfer Learning (2017, background): “JCI can be seen as a generalization of the idea behind the LCD algorithm [Cooper, 1997] to multiple variables.”
Foundations of structural causal models with cycles and latent variables (2016, methodology): “Also, Local Causal Discovery (LCD) [9], Y-structures [35] and the Joint Causal Inference framework (JCI) all apply to simple SCMs [44] even though they were originally developed for acyclic SCMs only.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “In 1997 Cooper andcolleagues introduced and applied the heuristic method K2MB for finding the Markov blanket of a target variable in the task of predicting pneumonia mortality (Cooper, 1997).”