[Bayesian Analysis in Expert Systems]: Comment: Graphical Models, Causality and Intervention

Causal Diagrams for Empirical Research (With Discussions)

Pearl, J. (2022) · ACM eBooks · doi:10.1145/3501714.3501734 · Cited by 19

Causal unsupervised semantic segmentation

Kim, J., Lee, B., & Ro, Y. M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112173 · Cited by 18

Analysing and modelling runtime architectural stability for self-adaptive software

Salama, M., & Rami Bahsoon, A. (2017) · Journal of Systems and Software · doi:10.1016/j.jss.2017.07.041 · Cited by 18

On a hypergraph probabilistic graphical model

Javidian, M. A., Wang, Z., Lü, L., & Valtorta, M. (2020) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-020-09701-7 · Cited by 17

Complexity visualization, dataset acquisition, and machine-learning perspectives for low-temperature plasma: a review

Sakai, O., Kawaguchi, S., & Murakami, T. (2022) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/ac76fa · Cited by 17

Situated information flow theory

Benthall, S. (2019) · Symposium on Hot Topics in the Science of Security (HotSoS) · doi:10.1145/3314058.3314066 · Cited by 16

Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variablesPreprint

Runge, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10324 · Cited by 15

Econometric causality: The central role of thought experiments

Heckman, J. J., & Pinto, R. R. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105719 · Cited by 14

A Comparative Study of Six Formal Models of Causal Ascription

Salem Benferhat, A., Bonnefon, J., Chassy, P., Neves, R. D. S., Didier Dubois, A., de Saint-Cyr, F. D., Kayser, D., Nouioua, F., Boutouhami, S., Prade, H. M., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87993-0_6 · Cited by 14

SemCaDo: A serendipitous strategy for causal discovery and ontology evolution

Messaoud, M. B., Leray, P., & Amor, N. B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.12.006 · Cited by 14

Repetitive causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders

Maeda, T., & Shimizu, S. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00282-0 · Cited by 12

Causal impact analysis of enhanced phosphorus effluent standard on river water quality

Kim, S., & Chung, S. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.115931 · Cited by 12

Graphical Models for Probabilistic and Causal Reasoning

Pearl, J. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b16812-50 · Cited by 12

Confounding and Causal Path Diagrams

Law, G. R., Green, R., & Ellison, G. T. H. (2012) · Springer eBooks · doi:10.1007/978-94-007-3024-3_1 · Cited by 12

Using Directed Acyclic Graphs (DAGs) to Determine if the Total Causal Effect of an Individual Randomized Physical Activity-Promoting Intervention is Identifiable

Myers, N. D., Brincks, A. M., & Lee, S. (2024) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2024.2318355 · Cited by 11

Risk aggregation in the presence of discrete causally connected random variables

Lin, P., Neil, M., & Fenton, N. (2014) · Annals of Actuarial Science · doi:10.1017/s1748499514000098 · Cited by 11

Narrative Creativity

Fletcher, A., & Benveniste, M. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009614801 · Cited by 13

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

Mohammad-Taheri, S., Zucker, J., Hoyt, C. T., Sachs, K., Tewari, V., Ness, R. O., & Vitek, O. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac251 · Cited by 11

Conditions for Non‐confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams

Geng, Z., & LI, G. (2001) · Scandinavian Journal of Statistics · doi:10.1111/1467-9469.00087 · Cited by 10

Soils, scale, or elites? Biological innovation in Uruguayan cattle farming, 1880–1913

Travieso, E. (2022) · The Economic History Review · doi:10.1111/ehr.13196 · Cited by 9

Causality from bottom to top: a survey

Weinberg, A. I., Premebida, C., & Faria, D. R. (2025) · Machine Learning · doi:10.1007/s10994-025-06855-5 · Cited by 17

Probabilistic Causality and Detecting Collections of Interdependence Patterns

Ramsahai, R. R. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12006 · Cited by 8

Combining experts' causal judgments

Alrajeh, D., Chockler, H., & Halpern, J. Y. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103355 · Cited by 9

Detecting Latent Heterogeneity

Pearl, J. (2022) · ACM eBooks · doi:10.1145/3501714.3501742 · Cited by 10

Belief Change in OCF-Based Networks in Presence of Sequences of Observations and Interventions: Application to Alert Correlation

Salem Benferhat, A., & Tabia, K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15246-7_5 · Cited by 9

On the Identification of Nonparametric Structural Models

Pearl, J. (1997) · Lecture notes in statistics · doi:10.1007/978-1-4612-1842-5_3 · Cited by 9

A Causal Calculus for Statistical Research

Pearl, J. (1996) · Lecture notes in statistics · doi:10.1007/978-1-4612-2404-4_3 · Cited by 9

Supplementary Variables for Causal Estimation

Ramsahai, R. R. (2012) · Wiley series in probability and statistics · doi:10.1002/9781119945710.ch16 · Cited by 9

Direct and Indirect Causal Effects of an Individual Randomized Physical Activity-Promoting Intervention: A Substantive-Methodological Synergy

Myers, N. D., Brincks, A. M., & Lee, S. (2024) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2024.2392563 · Cited by 8

Emulating Target Trials to Study Perioperative and Critical Care Interventions with Observational Data: Promise and Limitations

Messinger, C. J., Bateman, B. T., & Wanis, K. N. (2025) · Anesthesiology · doi:10.1097/aln.0000000000005308 · Cited by 9

Necessity causality in mental health research: Applying necessary condition analysis in clinical psychology and psychiatry

Marchetti, I., Koster, E. H. W., & Dul, J. (2025) · Clinical Psychology Review · doi:10.1016/j.cpr.2025.102689 · Cited by 10

Associations Between Vocal Arousal and Dyadic Coping During Couple Interactions After a Stress Induction

Bulling, L. J., Hilpert, P., Bertschi, I. C., Ivic, A., & Bodenmann, G. (2023) · International Journal of Applied Positive Psychology · doi:10.1007/s41042-023-00087-5 · Cited by 8

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series

Castri, L., Mghames, S., Hanheide, M., & Bellotto, N. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202400181 · Cited by 10

Bayesian networks utilization for reliability evaluation of power systems

Ciobanu, A., Munteanu, F., & Nemeș, C. M. (2016) · International Conference and Exposition on Electrical and Power Engineering (EPE) · doi:10.1109/icepe.2016.7781454 · Cited by 7

Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance

Ni, J., Jin, Z., Freitag, M., Sachan, M., & Schölkopf, B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.389 · Cited by 7

Addressing challenges in uncertainty quantification: the case of geohazard assessments

Cárdenas, I. C., Aven, T., & Flage, R. (2023) · Geoscientific model development · doi:10.5194/gmd-16-1601-2023 · Cited by 7

An Interventionist’s Guide to Exotic Choice

Stern, R. (2020) · Mind · doi:10.1093/mind/fzaa082 · Cited by 7

Why Do You Take That Route?

Nabijiang, A. (2020) · doi:10.31390/gradschool_theses.5132 · Cited by 7

Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”

Pearl, J. (2022) · Journal of Causal Inference · doi:10.1515/jci-2022-0046 · Cited by 6

Long-term causal effects estimation via latent surrogates representation learning

Cai, R., Chen, W., Yang, Z., Wan, S., Zheng, C., Yang, X., & Guo, J. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106336 · Cited by 6

Introduction to causal graphs for education researchers

Feng, Y. (2024) · Asia Pacific Education Review · doi:10.1007/s12564-024-09980-3 · Cited by 6

What do we want to estimate from observational datasets? Choosing appropriate statistical analysis methods based on the chemical management phase

Takeshita, K. M., Hayashi, T. I., & Yokomizo, H. (2021) · Integrated Environmental Assessment and Management · doi:10.1002/ieam.4564 · Cited by 6

Covariate selection in causal learning under non-Gaussianity

Zhang, B., & Wiedermann, W. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02217-y · Cited by 6

Peer Relationships Are a Direct Cause of the Adolescent Mental Health Crisis: Interpretable Machine Learning Analysis of 2 Large Cohort Studies

Stuke, H., Schlack, R., Erhart, M., Kaman, A., Ravens‐Sieberer, U., & Irrgang, C. (2025) · JMIR Public Health and Surveillance · doi:10.2196/60125 · Cited by 7

Quantifying interventional causality by knockoff operation

Zhang, X., & Chen, L. (2025) · Science Advances · doi:10.1126/sciadv.adu6464 · Cited by 5

Leveraging Causal Reasoning in Educational Data Mining: An Analysis of Brazilian Secondary Education

Filho, R. L. C. S., Brito, K. D. S., & Adeodato, P. J. L. (2023) · Applied Sciences · doi:10.3390/app13085198 · Cited by 6

So Many Choices: A Guide to Selecting Among Methods to Adjust for Observed Confounders

Keele, L. J., & Grieve, R. (2025) · Statistics in Medicine · doi:10.1002/sim.10336 · Cited by 9

Handling manipulated evidence

Baio, G., & Corradi, F. (2006) · Forensic Science International · doi:10.1016/j.forsciint.2006.08.017 · Cited by 5

Active learning of causal Bayesian networks using ontologies: A case study

Messaoud, M. B., Leray, P., & Amor, N. B. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706815 · Cited by 5

The variance of causal effect estimators for binary v-structures

Kuipers, J., & Moffa, G. (2022) · Journal of Causal Inference · doi:10.1515/jci-2021-0025 · Cited by 5

IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION

Valtorta, M., & Huang, Y. (2008) · Cybernetics & Systems · doi:10.1080/01969720802039594 · Cited by 5

Econometric Causality: The Central Role of Thought Experiments

Heckman, J. J., & Pinto, R. R. (2023) · National Bureau of Economic Research · doi:10.3386/w31945 · Cited by 5

Performing Bayesian Risk Aggregation using Discrete Approximation Algorithms with Graph FactorizationPreprint

Lin, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.01056 · Cited by 5

Data - driven Bayesian networks for reliability of supply from renewable sources

Ciobanu, A., Munteanu, F., Nemeș, C. M., & Astanei, D. G. (2017) · International Aegean Conference on Electrical Machines and Power Electronics & International Conference on Optimization of Electrical and Electronic Equipment (ACEMP-OPTIM) · doi:10.1109/optim.2017.7974952 · Cited by 4

Time to Consider Time, and Time to Predict?

Arjas, E. (2013) · Statistics in Biosciences · doi:10.1007/s12561-013-9101-1 · Cited by 4

Covariate selection criteria for controlling confounding bias in a causal study

Thepepomma, S., & Kim, J.-H. (2016) · Korean Journal of Applied Statistics · doi:10.5351/kjas.2016.29.5.849 · Cited by 2

Decision and Intervention

Stern, R. (2018) · Erkenntnis · doi:10.1007/s10670-018-9980-0 · Cited by 4

Strategic Responses to Peer-Firm Financial Flexibility: Evidence from Indirect Treatment EffectsPreprint

Nordlund, J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2866505 · Cited by 4

Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning

Franklin, M. K., Peasgood, T., & Tennant, P. W. G. (2025) · Quality of Life Research · doi:10.1007/s11136-025-04007-9 · Cited by 3

All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network

Wang, B., Qin, B., Li, J., Xu, F., Sun, F., & Xiong, H. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3675478 · Cited by 3

Interventional Training for Out-Of-Distribution Natural Language Understanding

Yu, S., Jiang, J., Zhang, H., Niu, Y., Sun, Q., & Bing, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.799 · Cited by 3

The importance of household composition in epidemiological analyses of sleep: Evidence from the Understanding Society longitudinal panel survey

Fowler, H., Ellison, G. T. H., SCOTT, E. M., & Law, G. R. (2014) · Open Journal of Epidemiology · doi:10.4236/ojepi.2014.41009 · Cited by 3

Bagged random causal networks for interventional queries on observational biomedical datasets

Prosperi, M., Guo, Y. M., & Bian, J. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103689 · Cited by 3

Causal Directed Acyclic Graphs

Foraita, R., Spallek, J., & Zeeb, H. (2023) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_65-1 · Cited by 3

Adding Local Constraints to Bayesian Networks

Crowley, M., Boerlage, B., & Poole, D. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72665-4_30 · Cited by 3

Subjectivity, Bayesianism, and causality

Ortega, P. A. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.04.018 · Cited by 3

How Mega Is the Mega? Measuring the Spillover Effects of WeChat by Machine Learning and EconometricsPreprint

Zheng, J., Qi, Z., & Tan, Y. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2842138 · Cited by 3

Confounding Equivalence in Causal Inference

Pearl, J., & Paz, A. (2014) · Journal of Causal Inference · doi:10.1515/jci-2013-0020 · Cited by 35

Does the type of stressor moderate the stress-snacking relationship? An intensive-longitudinal study

Lüthi, S., Bamert, M., & Inauen, J. (2025) · Appetite · doi:10.1016/j.appet.2025.108145 · Cited by 3

Preventing Spurious Interactions: A New Inductive Bias for Accurate Treatment Effect Estimation

Pros, R., & Vitria, J. M. (2025) · IEEE Access · doi:10.1109/access.2025.3574547 · Cited by 2

Cluster allocation design networks

Madrigal, A. M. (2007) · Bayesian Analysis · doi:10.1214/07-ba222 · Cited by 1

Probing potential priming: Defining, quantifying, and testing the causal priming effect using the potential outcomes framework

Chén, O. Y., Phan, H., Cao, H., Qian, T., Nagels, G., & Vos, M. D. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.724498 · Cited by 2

Bayesian projections of total fertility rate conditional on the United Nations sustainable development goals

Liu, D. H., & Raftery, A. E. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1793 · Cited by 1

Identification of causal influences in quantum processes

Friend, I., & Kissinger, A. (2024) · Physical Review A · doi:10.1103/physreva.109.042214 · Cited by 2

Identifying Causal Effects Under Functional Dependencies

Chen, Y., & Darwiche, A. Y. (2024) · Entropy · doi:10.3390/e26121061 · Cited by 2

Statistics, Causality, and Graphs

Pearl, J. (1999) · Springer eBooks · doi:10.1007/978-3-642-58648-4_1 · Cited by 2

Identifying intervention variables

Baumgartner, M., & Drouet, I. (2013) · European Journal for Philosophy of Science · doi:10.1007/s13194-012-0063-9 · Cited by 2

Autoregressive Statistical Modeling of a Peru Margin Multi-proxy Holocene Record Shows Correlation Not Causation, Flickering Regimes and Persistence

Ahn, S., Fox‐Kemper, B., Herbert, T. D., Altabet, M. A., & Lawrence, C. E. (2019) · Journal of Statistical Physics · doi:10.1007/s10955-019-02391-4 · Cited by 2

Causal Inference–Based Study of Key Contributors to Industrial Accidents

Okki, S., Chebila, M., & Nait-Said, R. (2023) · ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering · doi:10.1061/ajrua6.rueng-1116 · Cited by 3

Dependability Modelling of Repairable Systems using Bayesian Networks

Neil, M., & Marquez, D. (2009) · IFAC Proceedings Volumes · doi:10.3182/20090610-3-it-4004.00043 · Cited by 2

On testing whether an Embedded Bayesian Network represents a probability model

Geiger, D., Paz, A., & Pearl, J. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-332-5.50036-5 · Cited by 2

Detecting Latent HeterogeneityPreprint

Pearl, J. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2343829 · Cited by 2

Kausale Interpretation von Graphen

Kischka, P. (1998) · Beiträge zur Wirtschaftsinformatik · doi:10.1007/978-3-642-86094-2_8 · Cited by 2

Making High-Dimensional Molecular Distribution Functions Tractable through Belief Propagation on Factor Graphs

Smith, Z., & Tiwary, P. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c05717 · Cited by 1

Spatio-temporal graphical counterfactuals: an overview

Kang, M., Chen, D., Pu, Z., Gao, J., & Yu, W. (2026) · Science China Information Sciences · doi:10.1007/s11432-024-4752-6 · Cited by 3

Perceptual saturation in deterrence: Examining the nonlinear relationships between arrest rate signal and perceptions of risk and reward

Altikriti, S., Petrich, D. M., & Anderson, C. N. (2024) · Journal of criminal justice · doi:10.1016/j.jcrimjus.2024.102245 · Cited by 1

A Decade of Machine Learning Applied to Management and Economics: Learning through a Case Study of Corporate Resilience

Bughin, J. (2025) · Business, management and economics · doi:10.5772/intechopen.1008856 · Cited by 1

Non-linéarités et interactions entre variables : quelques apports des méthodes d’apprentissage automatique interprétables à la régulation bancaire

Durand, P., Quang, G. L., & Vialfont, A. (2025) · Revue d économie politique · doi:10.3917/redp.346.0893 · Cited by 1

The connection between Bayesian networks and adverse outcome pathway (AOP) networks and how to use it for predicting drug toxicity

Wang, D., Suzuki, A., & Tong, W. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104350 · Cited by 1

Quantifying hydrogen technology acceptance: Insights from Bayesian networks

Herr, D. Z., Scovell, M., Kinaev, N. N., & Vaisman, R. (2025) · Energy and Climate Change · doi:10.1016/j.egycc.2025.100201 · Cited by 1

Belief Revision of Product-Based Causal Possibilistic Networks

Salem Benferhat, A., & Tabia, K. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13059-5_24 · Cited by 1

Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial

Keele, L. J., O’Neill, S., & Grieve, R. (2021) · Medical Decision Making · doi:10.1177/0272989x20986545 · Cited by 2

Do Causal Concentration-Response Functions Exist?

Cox, L. A. (2020) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-030-57358-4_15 · Cited by 1

Variable elimination, graph reduction and the efficient g-formula

Guo, F. R., Perković, E., & Rotnitzky, A. G. (2022) · Biometrika · doi:10.1093/biomet/asac062 · Cited by 1

Confounder selection via iterative graph expansion

Guo, F. R., & Zhao, Q. (2026) · The Annals of Statistics · doi:10.1214/25-aos2577 · Cited by 9

Causal Models and Metaphysics – Part 1: Using Causal Models

McDonald, J. (2024) · Philosophy Compass · doi:10.1111/phc3.12975 · Cited by 1

CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing

Dou, S., Gao, S., Gui, T., & Zhang, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-9431-7_22 · Cited by 1

Exploiting Causal Knowledge During CATE Estimation Using Tree Based Metalearners

Pros, R., & Vitria, J. M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74640-6_19 · Cited by 1

Causal Concepts, Principles, and Algorithms

Cox, L. A., Popken, D. A., & Sun, R. X. (2018) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-319-78242-3_2 · Cited by 1

Continuum fault trees

Taylor, J. R., & KOZINE, I. O. (2022) · Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability · doi:10.1177/1748006x221115241 · Cited by 1

[Bayesian Analysis in Expert Systems]: Comment: Assessing the Science Behind Graphical Modelling Techniques

Dempster, A. P. (1993) · Statistical Science · doi:10.1214/ss/1177010889

[Bayesian Analysis in Expert Systems]: Comment: Conditional Independence and Causal Inference

Glymour, C., & Spirtes, P. (1993) · Statistical Science · doi:10.1214/ss/1177010890 · Cited by 1

Graphical models and causality, part 3

Maathuis, M. H. (2014)

Graphical models for causal inference

Lauritzen, S. L. (2009) · Cited by 8

Methods for Graphical Models and Causal Inference

Kalisch, M., Hauser, A., & Maechler, M. (2015) · Cited by 1