Predicting causal effects in large-scale systems from observational data

Regression shrinkage and selection via the lasso

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Regularization and Variable Selection Via the Elastic Net

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CAUSALITY: MODELS, REASONING AND INFERENCE

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Causation, Prediction, and Search

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Functional Discovery via a Compendium of Expression Profiles

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Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

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Estimating high-dimensional intervention effects from observational data

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Causal Inference Using Graphical Models with the R Package pcalg

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Estimating individual treatment effect: generalization bounds and algorithmsPreprint

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Order-independent constraint-based causal structure learningPreprint

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Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

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From hype to reality: data science enabling personalized medicine

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Learning high-dimensional directed acyclic graphs with latent and selection variables

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Computational solutions for omics data

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High-Dimensional Statistics with a View Toward Applications in Biology

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Deep learning for inferring gene relationships from single-cell expression data

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Linguistic Diversity and Traffic Accidents: Lessons from Statistical Studies of Cultural Traits

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Inferring causal molecular networks: empirical assessment through a community-based effort

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Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding

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Structure Learning in Graphical Modeling

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Causal Structure Learning

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Exploring the psychology of suicidal ideation: a theory driven network analysis.

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Gene regulatory network inference resources: A practical overview

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Causal Confusion in Imitation LearningPreprint

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Methods for causal inference from gene perturbation experiments and validation

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A Fast PC Algorithm for High Dimensional Causal Discovery with Multi-Core PCs

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Invariance, Causality and Robustness

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MIDER: Network Inference with Mutual Information Distance and Entropy Reduction

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Lingam: Non-Gaussian Methods for Estimating Causal Structures

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Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

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$\ell_{0}$-penalized maximum likelihood for sparse directed acyclic graphs

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PC algorithm for nonparanormal graphical models

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Structural Intervention Distance for Evaluating Causal Graphs

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High-dimensional consistency in score-based and hybrid structure learning

Nandy, P., Hauser, A., & Maathuis, M. H. (2018) · The Annals of Statistics · doi:10.1214/17-aos1654 · Cited by 104 · see citing works

Lipid Profiles and Heart Failure Risk

Wittenbecher, C., Eichelmann, F., Toledo, E., Guasch‐Ferré, M., Ruiz‐Canela, M., Li, J., Arós, F., Lee, C., Liang, L., Salas‐Salvadó, J., et al. (2020) · Circulation Research · doi:10.1161/circresaha.120.317883 · Cited by 98 · see citing works

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

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A generalized back-door criterion

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

Hackett, S. R., Baltz, E. A., Coram, M., Wranik, B. J., Kim, G., Baker, A., Fan, M., Hendrickson, D. G., Berndl, M., & McIsaac, R. S. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199174 · Cited by 75 · see citing works

Inferring microRNA–mRNA causal regulatory relationships from expression data

Le, T. D., Liu, L., Tsykin, A., Goodall, G. J., Liu, B., Sun, B., & Li, J. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt048 · Cited by 80 · see citing works

A review of active learning approaches to experimental design for uncovering biological networks

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Deep Brain Stimulation Programming 2.0: Future Perspectives for Target Identification and Adaptive Closed Loop Stimulation

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Causal stability ranking

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Investigating the importance of self-theories of intelligence and musicality for students' academic and musical achievement

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Biological network analysis: insights into structure and functions

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LncmiRSRN: identification and analysis of long non-coding RNA related miRNA sponge regulatory network in human cancer

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Dihydroceramide- and ceramide-profiling provides insights into human cardiometabolic disease etiology

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Graphical Criteria for Efficient Total Effect Estimation Via Adjustment in Causal Linear Models

Henckel, L., Perković, E., & Maathuis, M. H. (2022) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12451 · Cited by 67 · see citing works

Inferring and analyzing module-specific lncRNA–mRNA causal regulatory networks in human cancer

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Estimating the effect of joint interventions from observational data in sparse high-dimensional settingsPreprint

Nandy, P., Maathuis, M. H., & Richardson, T. S. (2014) · Cited by 54 · see citing works

Causal discovery for the microbiome

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Systems microscopy: An emerging strategy for the life sciences

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Copula PC Algorithm for Causal Discovery from Mixed Data

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Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region

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Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

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Changes in metabolomics profiles over ten years and subsequent risk of developing type 2 diabetes: Results from the Nurses' Health Study

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From miRNA regulation to miRNA-TF co-regulation: computational approaches and challenges

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Modeling the impacts of urbanization on watershed-scale gross primary productivity and tradeoffs with water yield across the conterminous United States

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Operator-valued kernel-based vector autoregressive models for network inference

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Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach

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Development of a new framework to identify pathways from socioeconomic development to environmental pollution

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Group Bound: Confidence Intervals for Groups of Variables in Sparse High Dimensional Regression Without Assumptions on the Design

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Invited Commentary: Off-Roading With Social Epidemiology—Exploration, Causation, Translation

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Probabilistic Computational Causal Discovery for Systems Biology

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Optimal design of gene knockout experiments for gene regulatory network inference

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Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

Shojaie, A., Jauhiainen, A., Kallitsis, M., & Michailidis, G. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0082393 · Cited by 35 · see citing works

Causal Structure Learning and Inference: A Selective Review

Kalisch, M., & Bühlmann, P. (2014) · Quality Technology & Quantitative Management · doi:10.1080/16843703.2014.11673322 · Cited by 36 · see citing works

Causal discovery through MAP selection of stratified chain event graphs

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Statistical Evidence Suggests that Inattention Drives Hyperactivity/Impulsivity in Attention Deficit-Hyperactivity Disorder

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cuPC: CUDA-Based Parallel PC Algorithm for Causal Structure Learning on GPU

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Multi-Domain Sampling With Applications to Structural Inference of Bayesian Networks

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PenPC: A Two-Step Approach to Estimate the Skeletons of High-Dimensional Directed Acyclic Graphs

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Causal Modeling of Cancer-Stromal Communication Identifies PAPPA as a Novel Stroma-Secreted Factor Activating NFκB Signaling in Hepatocellular Carcinoma

Engelmann, J. C., Amann, T., Ott-Rötzer, B., Nuetzel, M., Reinders, Y., Reinders, J., Thasler, W. E., Kristl, T., Teufel, A., Huber, C. G., et al. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004293 · Cited by 32 · see citing works

miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships

Le, T. D., Zhang, J., Liu, L., Liu, H., & Li, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0145386 · Cited by 33 · see citing works

Joint estimation of causal effects from observational and intervention gene expression data

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How difficult is inference of mammalian causal gene regulatory networks?

Djordjevic, D., Yang, A., Zadoorian, A., Rungrugeecharoen, K., & Ho, J. W. K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111661 · Cited by 29 · see citing works

Ensemble Methods for MiRNA Target Prediction from Expression Data

Le, T. D., Zhang, J., Liu, L., & Li, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131627 · Cited by 30 · see citing works

Towards integrative causal analysis of heterogeneous data sets and studies

Tsamardinos, I., Triantafillou, S., & Lagani, V. (2012) · Cited by 29 · see citing works

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

Delucchi, M., Spinner, G. R., Scutari, M., Bijlenga, P., Morel, S., Friedrich, C. M., Furrer, R., & Hirsch, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105740 · Cited by 29 · see citing works

Causal interpretation of stochastic differential equations

Hansen, N. R., & Sokol, A. (2014) · Electronic Journal of Probability · doi:10.1214/ejp.v19-2891 · Cited by 29 · see citing works

On efficient adjustment in causal graphsPreprint

Witte, J., Henckel, L., Maathuis, M. H., & Didelez, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06825 · Cited by 29 · see citing works

Identifying direct miRNA–mRNA causal regulatory relationships in heterogeneous data

Zhang, J., Le, T. D., Liu, L., Liu, B., He, J., Goodall, G. J., & Li, J. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.08.005 · Cited by 28 · see citing works

Fast and efficient dynamic nested effects models

Fröhlich, H., Praveen, P., & Tresch, A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq631 · Cited by 28 · see citing works

A review of materials selection for optimized efficiency in quantum dot sensitized solar cells: A simplified approach to reviewing literature data

Rauf, I. A., & Rezai, P. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.01.137 · Cited by 27 · see citing works

Robust Causal Structure Learning with Some Hidden Variables

Frot, B., Nandy, P., & Maathuis, M. H. (2019) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12315 · Cited by 27 · see citing works

Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression

Rakitsch, B., & Stegle, O. (2016) · Genome biology · doi:10.1186/s13059-016-0895-2 · Cited by 24 · see citing works

Reconstructing Causal Biological Networks through Active Learning

Cho, H., Berger, B., & Peng, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150611 · Cited by 25 · see citing works

Overcoming the matched-sample bottleneck: an orthogonal approach to integrate omic data

Nguyen, T., Diaz, D., Tagett, R., & Drăghici, S. (2016) · Scientific Reports · doi:10.1038/srep29251 · Cited by 25 · see citing works

Inferring condition-specific miRNA activity from matched miRNA and mRNA expression data

Zhang, J., Le, T. D., Liu, L., Liu, B. Y., He, J., Goodall, G. J., & Li, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu489 · Cited by 25 · see citing works

The UKB envirome of depression: from interactions to synergistic effects

Hullám, G., Antal, P., Petschner, P., Gonda, X., Bagdy, G., Deakin, J. F. W., & Juhász, G. (2019) · Scientific Reports · doi:10.1038/s41598-019-46001-5 · Cited by 27 · see citing works

A review of causal discovery methods for molecular network analysis

Kelly, J., Berzuini, C., Keavney, B. D., Tomaszewski, M., & Guo, H. (2022) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.2055 · Cited by 22 · see citing works

Estimating bounds on causal effects in high-dimensional and possibly confounded systems

Malinsky, D., & Spirtes, P. (2017) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2017.06.005 · Cited by 24 · see citing works

Causal Discovery of Gene Regulation with Incomplete Data

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DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks

Shutta, K. H., Weighill, D. A., Burkholz, R., Guebila, M. B., DeMeo, D. L., Zacharias, H. U., Quackenbush, J. F., & Altenbuchinger, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1157 · Cited by 23 · see citing works

Interpreting and using CPDAGs with background knowledgePreprint

Perković, E., Kalisch, M., & Maathuis, M. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.02171 · Cited by 27 · see citing works

THE EFFECT OF UNMEASURED CONFOUNDERS ON THE ABILITY TO ESTIMATE A TRUE PERFORMANCE OR SELECTION GRADIENT (AND OTHER PARTIAL REGRESSION COEFFICIENTS)

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A review on methods for predicting miRNA–mRNA regulatory modules

Madhumita, M., & Paul, S. (2022) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2020-0048 · Cited by 22 · see citing works

Causal statistical inference in high dimensions

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Data mining analysis of human gut microbiota links Fusobacterium spp. with colorectal cancer onset

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A multi-source data integration approach reveals novel associations between metabolites and renal outcomes in the German Chronic Kidney Disease study

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Comorbidity networks: beyond disease correlations

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A uniformly consistent estimator of causal effects under the k-Triangle-Faithfulness assumption

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Dissecting the effect of heat stress on durum wheat under field conditions

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