Learning Causal Networks Research Articles

Continuity and Discontinuity in Basic Education Learning: Causality Isolation Policy in Avoiding the Spread of COVID-19

The COVID-19 outbreak is a non-natural disaster and has an impact on learning continuity. This study aimed to find the reality of the continuity/discontinuity of learning of primary education students in East Lombok during student isolation in the prevention of COVID-19. Data obtained through surveys and interviews with 198 respondents representing school principals, teachers, students, parents, and the community. According to the Bayesian Approach to Learning Causal Networks, the data were analyzed descriptively through frequency distribution and causal analysis. It was found that 57.07 % of respondents claimed they did not know the purpose of the isolation policy, so it was not explained. Exactly 54.76 % of respondents admitted to isolation as a holiday, 54 % of respondents admitted to playing to their heart's content during the isolation period, 88.77 % of respondents admitted that this policy was not coordinated, and 100 % of respondents admitted that they did not maximize the function of online learning. The contextual condition of this isolation period is a causal event between a lack of understanding of the policy intent or P (A), causing unclear instructions, or P (B). Unclear teacher instruction or P (B) causes students not to study at home during isolation, or learning discontinuity occurs, with code P (C). Lack of understanding of the intent of the isolation policy, or P (A) causes this policy not to be coordinated with interested parties. Due to unclear instructions, the utilization of online learning, or P (E) is not optimal. The study results are urgent and have implications for the policy of learning from home as well as the coordination pattern of educational stakeholders in the COVID-19 phase.

Learning causal networks using inducible transcription factors and transcriptome‐wide time series

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

Learning causal networks with latent variables from multivariate information in genomic data.

Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.

Using observed sequence to orient causal networks.

In learning causal networks, typically cross-sectional data are used and the sequence among the network nodes is learned through conditional independence. Sequence is inherently a longitudinal concept. We propose to learn sequence of events in longitudinal data and use it to orient arc directions in a network learned from cross-sectional data. The network is learned from cross-sectional data using various established algorithms, with one modification. Arc directions that do not agree with the longitudinal sequence were prohibited. We established longitudinal sequence through two methods: Probabilistic Contrast, and Goodman and Kruskal error reduction methods. In simulated data, the error reduction method was used to learn the sequence in the data. The procedure reduced the number of arc direction errors and larger improvements were observed with increasing number of events in the network. In real data, different algorithms were used to learn the network from cross-sectional data, while prohibiting arc directions not supported by longitudinal information. The agreement among learned networks increased significantly. It is possible to combine sequence information learned from longitudinal data with algorithms organized for learning network models from cross-sectional data. Such models may have additional causal interpretation as they more explicitly take into account observed sequence of events.

A psychological approach to learning causal networks

We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive. The heuristic increased predictive accuracy by an average of 0.51 % percent, a small amount. It also increased total agreement between different network learning algorithms (Max Spanning Tree, Taboo, EQ, SopLeq, and Taboo Order) by 25 %. Prior to use of the heuristic, the multiple raters Kappa between the algorithms was 0.60 (95 % confidence interval, CI, from 0.53 to 0.67) indicating moderate agreement among the networks learned through different algorithms. After the use of the heuristic, the multiple raters Kappa was 0.85 (95 % CI from 0.78 to 0.92). There was a statistically significant increase in agreement between the five algorithms (alpha < 0.05). These data suggest that the heuristic increased agreement between networks learned through use of different algorithms, without loss of predictive accuracy. Additional research is needed to see if findings persist in other data sets and to explain why a heuristic used by humans could improve construct validity of mathematical algorithms.

Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent

Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures, and the presence of equivalence classes. In this article, we develop an L 1-penalized likelihood approach to estimate the structure of causal Gaussian networks. A blockwise coordinate descent algorithm, which takes advantage of the acyclicity constraint, is proposed for seeking a local maximizer of the penalized likelihood. We establish that model selection consistency for causal Gaussian networks can be achieved with the adaptive lasso penalty and sufficient experimental interventions. Simulation and real data examples are used to demonstrate the effectiveness of our method. In particular, our method shows satisfactory performance for DAGs with 200 nodes, which have about 20,000 free parameters. Supplementary materials for this article are available online.

Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregressive process

BackgroundCausal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning these networks is challenging due to the low sample size and high dimensionality of genomic data.ResultsWe present a novel and highly efficient approach to estimate a VAR network. This proceeds in two steps: (i) improved estimation of VAR regression coefficients using an analytic shrinkage approach, and (ii) subsequent model selection by testing the associated partial correlations. In simulations this approach outperformed for small sample size all other considered approaches in terms of true discovery rate (number of correctly identified edges relative to the significant edges). Moreover, the analysis of expression time series data from Arabidopsis thaliana resulted in a biologically sensible network.ConclusionStatistical learning of large-scale VAR causal models can be done efficiently by the proposed procedure, even in the difficult data situations prevalent in genomics and proteomics.AvailabilityThe method is implemented in R code that is available from the authors on request.

Learning causal networks from data a survey and a new algorithm for recovering possibilistic causal networks

Causal concepts play a crucial role in many reasoning tasks. Organised as a model revealing the causal structure of a domain, they can guide inference through relevant knowledge. This is an especia...