Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference), 2017
Discovering causal structure from observational data in the presence of latent variables remains ... more Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible to apply when modeling latent variables. We introduce a hybrid method that derives a Bayesian probability that the set of independence tests associated with a given causal model are jointly correct. Using this constraint-based scoring method, we are able to score multiple causal models, which possibly contain latent variables, and output the most probable one. The structure-discovery performance of the proposed method is compared to an existing constraint-based method (RFCI) using data generat...
If we assume that the process modelled is stable over time, then we can represent the causal stru... more If we assume that the process modelled is stable over time, then we can represent the causal structure of the series with a repeating graph that includes the smallest fragment of the series that repeats. The number of temporal slices in the repeating graph is the longest lag of direct influence plus one. For example, the repeating graph in Figure 2, which represents the series in Figure 1, needs three temporal slices to represent a repeating sequence, because V2 has a direct effect on V3 with a temporal lag of two.
Neumann et al. (2010) aim to find directed graphical representations of the independence and depe... more Neumann et al. (2010) aim to find directed graphical representations of the independence and dependence relations among activities in brain regions by applying a search procedure to merged fMRI activity records from a large number of contrasts obtained under a variety of conditions. To that end, Neumann et al., obtain three graphical models, justifying their search procedure with simulations that find that merging the data sampled from probability distributions characterized by two distinct Bayes net graphs results in a graphical object that combines the edges in the individual graphs. We argue that the graphical objects they obtain cannot be interpreted as representations of conditional independence and dependence relations among localized neural activities; specifically, directed edges and directed pathways in their graphical results may be artifacts of the manner in which separate studies are combined in the meta-analytic procedure. With a larger simulation study, we argue that their simulation results with combined data sets are an artifact of their choice of examples. We provide sufficient conditions and necessary conditions for the merger of two or more probability distributions, each characterized by the Markov equivalence class of a directed acyclic graph, to be describable by a Markov equivalence class whose edges are a union of those for the individual distributions. Contrary to Neumann et al., we argue that the scientific value of searches for network representations from imaging data lies in attempting to characterize large scaled neural mechanisms, and we suggest several alternative strategies for combining data from multiple experiments.
There is great interest in estimating brain &... more There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional…
Bayesian network analysis is an attractive approach for studying the functional integration of br... more Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.
Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions... more Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.
After reviewing theoretical reasons for doubting that machine learning methods can accurately inf... more After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared ...
Data sets with many discrete variables and relatively few cases arise in many domains. Several st... more Data sets with many discrete variables and relatively few cases arise in many domains. Several studies have sought to identify the Markov Blanket (MB) of a target variable by filtering variables using statistical decisions for conditional independence and ...
JCourse, created at Carnegie Mellon University, is a Java-and XML-based system for developing and... more JCourse, created at Carnegie Mellon University, is a Java-and XML-based system for developing and deploying web-based courses. On the development side, JCourse allows content providers to work more independently of web designers than has been previously possible. On the deployment side, JCourse provides basic (albeit incomplete) support for the IMS Question and Test Interoperability v1. 0 specification (www. imsproject. org), which allows questions and tests from one compliant system to be reused in other ...
Most causal discovery algorithms in the lit-erature exploit an assumption usually re-ferred to as... more Most causal discovery algorithms in the lit-erature exploit an assumption usually re-ferred to as the Causal Faithfulness or Sta-bility Condition. In this paper, we high-light two components of the condition used in constraint-based algorithms, which we call ...
Abstract: An important task in data analysis is the discovery of causal relationships between obs... more Abstract: An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on conditional independencies (Spirtes et al. 1993; Pearl 2000) cannot distinguish between independence-equivalent models, whereas approaches purely based on Independent ...
Robotic explorers, e.g. rovers, need to make crucial science decisions autonomously that are dist... more Robotic explorers, e.g. rovers, need to make crucial science decisions autonomously that are distinct from control, health, and navigation issues. We have investigated potential tools that can be applied to imaging and near-infrared spectral data.
Machine learning and knowledge discovery in databases : European Conference, ECML PKDD ... : proceedings. ECML PKDD (Conference), 2017
Discovering causal structure from observational data in the presence of latent variables remains ... more Discovering causal structure from observational data in the presence of latent variables remains an active research area. Constraint-based causal discovery algorithms are relatively efficient at discovering such causal models from data using independence tests. Typically, however, they derive and output only one such model. In contrast, Bayesian methods can generate and probabilistically score multiple models, outputting the most probable one; however, they are often computationally infeasible to apply when modeling latent variables. We introduce a hybrid method that derives a Bayesian probability that the set of independence tests associated with a given causal model are jointly correct. Using this constraint-based scoring method, we are able to score multiple causal models, which possibly contain latent variables, and output the most probable one. The structure-discovery performance of the proposed method is compared to an existing constraint-based method (RFCI) using data generat...
If we assume that the process modelled is stable over time, then we can represent the causal stru... more If we assume that the process modelled is stable over time, then we can represent the causal structure of the series with a repeating graph that includes the smallest fragment of the series that repeats. The number of temporal slices in the repeating graph is the longest lag of direct influence plus one. For example, the repeating graph in Figure 2, which represents the series in Figure 1, needs three temporal slices to represent a repeating sequence, because V2 has a direct effect on V3 with a temporal lag of two.
Neumann et al. (2010) aim to find directed graphical representations of the independence and depe... more Neumann et al. (2010) aim to find directed graphical representations of the independence and dependence relations among activities in brain regions by applying a search procedure to merged fMRI activity records from a large number of contrasts obtained under a variety of conditions. To that end, Neumann et al., obtain three graphical models, justifying their search procedure with simulations that find that merging the data sampled from probability distributions characterized by two distinct Bayes net graphs results in a graphical object that combines the edges in the individual graphs. We argue that the graphical objects they obtain cannot be interpreted as representations of conditional independence and dependence relations among localized neural activities; specifically, directed edges and directed pathways in their graphical results may be artifacts of the manner in which separate studies are combined in the meta-analytic procedure. With a larger simulation study, we argue that their simulation results with combined data sets are an artifact of their choice of examples. We provide sufficient conditions and necessary conditions for the merger of two or more probability distributions, each characterized by the Markov equivalence class of a directed acyclic graph, to be describable by a Markov equivalence class whose edges are a union of those for the individual distributions. Contrary to Neumann et al., we argue that the scientific value of searches for network representations from imaging data lies in attempting to characterize large scaled neural mechanisms, and we suggest several alternative strategies for combining data from multiple experiments.
There is great interest in estimating brain &... more There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional…
Bayesian network analysis is an attractive approach for studying the functional integration of br... more Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.
Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions... more Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.
After reviewing theoretical reasons for doubting that machine learning methods can accurately inf... more After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared ...
Data sets with many discrete variables and relatively few cases arise in many domains. Several st... more Data sets with many discrete variables and relatively few cases arise in many domains. Several studies have sought to identify the Markov Blanket (MB) of a target variable by filtering variables using statistical decisions for conditional independence and ...
JCourse, created at Carnegie Mellon University, is a Java-and XML-based system for developing and... more JCourse, created at Carnegie Mellon University, is a Java-and XML-based system for developing and deploying web-based courses. On the development side, JCourse allows content providers to work more independently of web designers than has been previously possible. On the deployment side, JCourse provides basic (albeit incomplete) support for the IMS Question and Test Interoperability v1. 0 specification (www. imsproject. org), which allows questions and tests from one compliant system to be reused in other ...
Most causal discovery algorithms in the lit-erature exploit an assumption usually re-ferred to as... more Most causal discovery algorithms in the lit-erature exploit an assumption usually re-ferred to as the Causal Faithfulness or Sta-bility Condition. In this paper, we high-light two components of the condition used in constraint-based algorithms, which we call ...
Abstract: An important task in data analysis is the discovery of causal relationships between obs... more Abstract: An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on conditional independencies (Spirtes et al. 1993; Pearl 2000) cannot distinguish between independence-equivalent models, whereas approaches purely based on Independent ...
Robotic explorers, e.g. rovers, need to make crucial science decisions autonomously that are dist... more Robotic explorers, e.g. rovers, need to make crucial science decisions autonomously that are distinct from control, health, and navigation issues. We have investigated potential tools that can be applied to imaging and near-infrared spectral data.
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Papers by Joseph Ramsey