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

Causal inference in statistics: An overview

Pearl, J. (2009) · Statistics Surveys · doi:10.1214/09-ss057 · Cited by 2,336

Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data

Rohrer, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917745629 · Cited by 1,002

The seven tools of causal inference, with reflections on machine learning

Pearl, J. (2019) · Communications of the ACM · doi:10.1145/3241036 · Cited by 659

Causal inference and the data-fusion problem

Bareinboim, E., & Pearl, J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510507113 · Cited by 621

Causal Interpretations of Black-Box Models

Zhao, Q., & Hastie, T. J. (2019) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2019.1624293 · Cited by 490

A guide to the literature on learning probabilistic networks from data

Buntine, W. (1996) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.494161 · Cited by 469

Econometric Methods for Program Evaluation

Abadie, A., & Cattaneo, M. D. (2018) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053402 · Cited by 458

External validity: From do-calculus to transportability across populations

Pearl, J., & Bareinboim, E. (2013) · Cited by 398

External Validity: From Do-Calculus to Transportability Across Populations

Pearl, J., & Bareinboim, E. (2014) · Statistical Science · doi:10.1214/14-sts486 · Cited by 455

Graphs, Causality, and Structural Equation Models

PEARL, J. (1998) · Sociological Methods & Research · doi:10.1177/0049124198027002004 · Cited by 341

Graphical Causal Models

Elwert, F. (2013) · Handbooks of Sociology and Social Research · doi:10.1007/978-94-007-6094-3_13 · Cited by 325

Chain Graph Models and their Causal Interpretations

Lauritzen, S. L., & Richardson, T. S. (2002) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00340 · Cited by 273

Influence Diagrams for Causal Modelling and Inference

Dawid, A. P. (2002) · International Statistical Review · doi:10.1111/j.1751-5823.2002.tb00354.x · Cited by 230

3. The Foundations of Causal Inference

Pearl, J. (2010) · Sociological Methodology · doi:10.1111/j.1467-9531.2010.01228.x · Cited by 245

Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks

Fenton, N., Neil, M., & Caballero, J. L. G. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.1073 · Cited by 236

Comment: understanding Simpson’s paradox

Pearl, J. (2014) · The American Statistician · doi:10.1080/00031305.2014.876829 · Cited by 206

How Do Climate and Catchment Attributes Influence Flood Generating Processes? A Large‐Sample Study for 671 Catchments Across the Contiguous USA

Stein, L., Clark, M., Knoben, W. J. M., Pianosi, F., & Woods, R. A. (2021) · Water Resources Research · doi:10.1029/2020wr028300 · Cited by 183

Ramification and causality

Thielscher, M. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(96)00033-1 · Cited by 181

Probabilities of causation: Bounds and identification

Tian, J., & Pearl, J. (2000) · Annals of Mathematics and Artificial Intelligence · doi:10.1023/a:1018912507879 · Cited by 179

Inference in hybrid Bayesian networks using dynamic discretization

Neil, M., Tailor, M., & Marquez, D. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9018-y · Cited by 175

From Association to Causation via Regression

Freedman, D. A. (1997) · Advances in Applied Mathematics · doi:10.1006/aama.1996.0501 · Cited by 175

Applying the structural causal model framework for observational causal inference in ecology

Arif, S., & MacNeil, M. A. (2022) · Ecological Monographs · doi:10.1002/ecm.1554 · Cited by 184

Identifiability, exchangeability and confounding revisited

Greenland, S., & Robins, J. M. (2009) · Epidemiologic Perspectives & Innovations · doi:10.1186/1742-5573-6-4 · Cited by 161

Causal Inference in Time Series Analysis

Eichler, M. (2012) · Wiley series in probability and statistics · doi:10.1002/9781119945710.ch22 · Cited by 146

Forecasting in social settings: The state of the art

Makridakis, S., Hyndman, R. J., & Petropoulos, F. (2019) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.05.011 · Cited by 170

Statistics and causal inference: A review

Pearl, J. (2003) · Test · doi:10.1007/bf02595718 · Cited by 144

Improved reliability modeling using Bayesian networks and dynamic discretization

Marquez, D., Neil, M., & Fenton, N. (2009) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2009.11.012 · Cited by 141

Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation

Love, P. E. D., Ahiaga-Dagbui, D. D., & Irani, Z. (2016) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2016.08.007 · Cited by 138

Graphical Models for Probabilistic and Causal Reasoning

Pearl, J. (1998) · Springer eBooks · doi:10.1007/978-94-017-1735-9_12 · Cited by 135

CAUSAL ANALYSIS AFTER HAAVELMO

Heckman, J. J., & Pinto, R. R. (2013) · Econometric Theory · doi:10.1017/s026646661400022x · Cited by 140

Causal Inference Methods for Combining Randomized Trials and Observational Studies: A Review

Colnet, B., Mayer, I., Chen, G., Dieng, A., Li, R., Varoquaux, G. P., Vert, J., Josse, J., & Yang, S. (2024) · Statistical Science · doi:10.1214/23-sts889 · Cited by 198

Probabilistic Evaluation of Counterfactual Queries

Balke, A., & Pearl, J. (2022) · ACM eBooks · doi:10.1145/3501714.3501733 · Cited by 124

Causal Inference Meets Deep Learning: A Comprehensive Survey

Jiao, L., Wang, Y., Liu, X., Li, L., Liu, F., Ma, W., Guo, Y., Chen, P., Yang, S., & Hou, B. (2024) · Research · doi:10.34133/research.0467 · Cited by 141

Signed Directed Acyclic Graphs for Causal Inference

VanderWeele, T. J., & Robins, J. M. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2009.00728.x · Cited by 107

Causal Inference in the Health Sciences: A Conceptual Introduction

Pearl, J. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020315127304 · Cited by 109

Identifying Direct and Indirect Effects in a Non-Counterfactual Framework

Geneletti, S. (2007) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00584.x · Cited by 108

A Probabilistic Calculus of Actions

Pearl, J. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-332-5.50062-6 · Cited by 106

Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification

Pearl, J. (1999) · Synthese · doi:10.1023/a:1005233831499 · Cited by 100

On Granger causality and the effect of interventions in time series

Eichler, M., & Didelez, V. (2009) · Lifetime Data Analysis · doi:10.1007/s10985-009-9143-3 · Cited by 93

Axioms of causal relevance

Galles, D., & Pearl, J. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00047-7 · Cited by 84

Collective Mining of Bayesian Networks from Distributed Heterogeneous Data

Chen, R., Sivakumar, K., & Kargupta, H. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-003-0107-8 · Cited by 86

The relation of age at menarche with age at natural menopause: a population study of 336 788 women in Norway

Bjelland, E. K., Hofvind, S. S. H., Byberg, L., & Eskild, A. (2018) · Human Reproduction · doi:10.1093/humrep/dey078 · Cited by 84

Analysis of Longitudinal Data with Drop-Out: Objectives, Assumptions and a Proposal

Diggle, P. J., Farewell, D. M., & Henderson, R. (2007) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2007.00590.x · Cited by 81

Graphical Models for Inference Under Outcome-Dependent Sampling

Didelez, V., Kreiner, S., & Keiding, N. (2010) · Statistical Science · doi:10.1214/10-sts340 · Cited by 80

Remarks on the method of propensity score

Pearl, J. (2009) · Statistics in Medicine · doi:10.1002/sim.3521 · Cited by 78

Causal Diversity and the Markov Condition

Cartwright, N. D. (1999) · Synthese · doi:10.1023/a:1005225629681 · Cited by 72

Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes

Pearl, J. (2018) · Journal of Causal Inference · doi:10.1515/jci-2018-2001 · Cited by 71

Complete mesocolic excision for right colonic cancer: prospective multicentre study

Benz, S. R., Feder, I. S., Vollmer, S., Tam, Y. C., Reinacher‐Schick, A., Denz, R., Hohenberger, W., Lippert, H., Tannapfel, A., & Stricker, I. (2022) · British journal of surgery · doi:10.1093/bjs/znac379 · Cited by 71

Causes of Effects and Effects of Causes

Pearl, J. (2014) · Sociological Methods & Research · doi:10.1177/0049124114562614 · Cited by 75

The roles, challenges, and merits of the p value

Chén, O. Y., Bodelet, J., Saraiva, R. G., Phan, H., Di, J., Nagels, G., Schwantje, T., Cao, H., Gou, J., Reinen, J., et al. (2023) · Patterns · doi:10.1016/j.patter.2023.100878 · Cited by 86

Gain Scores Revisited: A Graphical Models Perspective

Kim, Y., & Steiner, P. M. (2019) · Sociological Methods & Research · doi:10.1177/0049124119826155 · Cited by 74

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

Müllensiefen, D., Harrison, P. M. C., Caprini, F., & Fancourt, A. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01702 · Cited by 81

Modeling dependable systems using hybrid Bayesian networks

Neil, M., Tailor, M., Marque, D., Fenton, N., & Hearty, P. (2006) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2006.83 · Cited by 69

TRYGVE HAAVELMO AND THE EMERGENCE OF CAUSAL CALCULUS

Pearl, J. (2014) · Econometric Theory · doi:10.1017/s0266466614000231 · Cited by 64

Lord’s Paradox Revisited – (Oh Lord! Kumbaya!)

Pearl, J. (2016) · Journal of Causal Inference · doi:10.1515/jci-2016-0021 · Cited by 65

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 86

Modelling dependable systems using hybrid Bayesian networks

Neil, M., Tailor, M., Marquez, D., Fenton, N., & Hearty, P. (2007) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2007.03.009 · Cited by 57

Nonprobability Sampling and Causal Analysis

Köhler, U., Kreuter, F., & Stuart, E. A. (2018) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-030718-104951 · Cited by 64

Availability modelling of repairable systems using Bayesian networks

Neil, M., & Marquez, D. (2010) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2010.06.003 · Cited by 52

CAUSALITY FROM A DISTRIBUTIONAL ROBUSTNESS POINT OF VIEW

Meinshausen, N. (2018) · IEEE Data Science Workshop (DSW) · doi:10.1109/dsw.2018.8439889 · Cited by 51

Medical idioms for clinical Bayesian network development

Kyrimi, E., Neves, M. R., McLachlan, S., Neil, M., Marsh, W., & Fenton, N. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103495 · Cited by 50

Time-series estimation of the effects of natural experiments

White, H. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2005.07.013 · Cited by 48

Estimating counterfactuals for evaluation of ecological and conservation impact: an introduction to matching methods

Ribas, L. G. D. S., Pressey, R. L., & Bini, L. M. (2021) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12697 · Cited by 49

Bias in OLAP Queries

Salimi, B., Gehrke, J. E., & Suciu, D. M. (2018) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3183713.3196914 · Cited by 46

Causation, Action, and Counterfactuals

Pearl, J. (1997) · Springer eBooks · doi:10.1007/978-94-017-0487-8_18 · Cited by 46

Understanding Simpson's ParadoxPreprint

Pearl, J. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2343788 · Cited by 43

Toward a General Causal Framework for the Study of Racial Bias in Policing

Knox, D., & Mummolo, J. (2020) · Journal of Political Institutions and Political Economy · doi:10.1561/113.00000018 · Cited by 38

Do causal concentration–response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality

Cox, L. A. (2017) · Critical Reviews in Toxicology · doi:10.1080/10408444.2017.1311838 · Cited by 39

Decision-theoretic foundations for statistical causality

Dawid, A. P. (2021) · Journal of Causal Inference · doi:10.1515/jci-2020-0008 · Cited by 43

Sampling Bias and Logistic Models

McCullagh, P. J. (2008) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2007.00660.x · Cited by 38

Bracketing off the actors: Towards an action-centric research agenda

Pentland, B. T., Pentland, A., & Calantone, R. J. (2017) · Information and Organization · doi:10.1016/j.infoandorg.2017.06.001 · Cited by 36

Causal Interventional Training for Image Recognition

Qin, W., Zhang, H., Hong, R., Lim, E., & Sun, Q. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3136717 · Cited by 37

Comparing associative, statistical, and inferential reasoning accounts of human contingency learning

Pineño, O., & Miller, R. R. (2007) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470210601000680 · Cited by 37

Utilizing causal diagrams across quasi‐experimental approaches

Arif, S., & MacNeil, M. A. (2022) · Ecosphere · doi:10.1002/ecs2.4009 · Cited by 36

Detecting Latent Heterogeneity

Pearl, J. (2015) · Sociological Methods & Research · doi:10.1177/0049124115600597 · Cited by 37

The Decision‐Theoretic Approach to Causal Inference

Dawid, A. P. (2012) · Wiley series in probability and statistics · doi:10.1002/9781119945710.ch4 · Cited by 34

Comment: Understanding Simpson’s Paradox

Pearl, J. (2022) · ACM eBooks · doi:10.1145/3501714.3501738 · Cited by 38

Conceptual framework for investigating causal effects from observational data in livestock1

Bello, N. M., Ferreira, V. C. A., Gianola, D., & Rosa, G. J. M. (2018) · Journal of Animal Science · doi:10.1093/jas/sky277 · Cited by 36

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

Zhou, Q. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap10346 · Cited by 29

Students' Perceived Ease of Use of an Elearning Management System: An Exogenous or Endogenous Variable?

Pan, C.-C. “., Sivo, S. A., Gunter, G. A., & Cornell, R. A. (2005) · Journal of Educational Computing Research · doi:10.2190/7m4g-r742-w9ft-jx1j · Cited by 32

Interventionist decision theory

Stern, R. (2016) · Synthese · doi:10.1007/s11229-016-1133-x · Cited by 31

Simulating from marginal structural models with time‐dependent confounding

Havercroft, W. G., & Didelez, V. (2012) · Statistics in Medicine · doi:10.1002/sim.5472 · Cited by 31

Learning Causal Effects From Observational Data in Healthcare: A Review and Summary

Shi, J.-P., & Norgeot, B. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.864882 · Cited by 38

Solving dynamic fault trees using a new Hybrid Bayesian Network inference algorithm

Marquez, D., Neil, M., & Fenton, N. (2008) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2008.4602222 · Cited by 31

BREEDING AND GENETICS SYMPOSIUM: Inferring causal effects from observational data in livestock1,2

Rosa, G. J. M., & Valente, B. D. (2012) · Journal of Animal Science · doi:10.2527/jas.2012-5840 · Cited by 31

The Deductive Approach to Causal Inference

Pearl, J. (2014) · Journal of Causal Inference · doi:10.1515/jci-2014-0016 · Cited by 28

The Econometric Model for Causal Policy Analysis

Heckman, J. J., & Pinto, R. R. (2022) · Annual Review of Economics · doi:10.1146/annurev-economics-051520-015456 · Cited by 32

Probabilities of Causation: Three Counterfactual Interpretations and Their Identification

Pearl, J. (2022) · ACM eBooks · doi:10.1145/3501714.3501735 · Cited by 33

Sustained Negative Mental Health Outcomes Among Healthcare Workers Over the First Year of the COVID-19 Pandemic: A Prospective Cohort Study

Mediavilla, R., Fernández-Jiménez, E., Martínez-Morata, I., Jaramillo, F., Andreo-Jover, J., Morán‐Sánchez, I., Mascayano, F., Moreno‐Küstner, B., Minué, S., Ayuso‐Mateos, J. L., et al. (2022) · International Journal of Public Health · doi:10.3389/ijph.2022.1604553 · Cited by 27

Understanding unforeseen production downtimes in manufacturing processes using log data-driven causal reasoning

Hagedorn, C., Huegle, J., & Schlößer, R. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01952-x · Cited by 24

Pelvic pain after childbirth

Bjelland, E. K., Owe, K. M., Pingel, R., Kristiansson, P., Vangen, S., & Eberhard‐Gran, M. (2015) · Pain · doi:10.1097/j.pain.0000000000000427 · Cited by 24

Evaluating the lifestyle impact of China’s rural housing land consolidation with locational big data: A study of Chengdu

Liu, L., Gao, X. A., Zhuang, J., Wen, W., Yang, B., Cheng, W., Xiao, P., Yao, X., & Deng, O. (2020) · Land Use Policy · doi:10.1016/j.landusepol.2020.104623 · Cited by 24

Structural and Probabilistic Causality

Pearl, J. (1996) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60566-6 · Cited by 24

How often does random assignment fail? Estimates and recommendations

Goldberg, M. H. (2019) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2019.101351 · Cited by 23

Commonalities in the classical, collapsibility and counterfactual concepts of confounding

Newman, S. C. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2003.07.014 · Cited by 23

Causal inference of construction safety management measures towards workers’ safety behaviors: A multidimensional perspective

Zhang, Z., Li, H., Guo, H., Wu, Y., & Luo, Z. (2024) · Safety Science · doi:10.1016/j.ssci.2024.106432 · Cited by 21

The effect of intervention in nickel concentrations on benthic macroinvertebrates: A case study of statistical causal inference in ecotoxicology

Takeshita, K. M., Hayashi, T. I., & Yokomizo, H. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.115059 · Cited by 20

Sex difference and earthquake experience effects on earthquake victims

Yılmaz, V., Cangür, Ş., & Çelik, H. E. (2005) · Personality and Individual Differences · doi:10.1016/j.paid.2005.01.014 · Cited by 20

Discovering Causal Structures in Bayesian Gaussian Directed Acyclic Graph Models

Castelletti, F., & Consonni, G. (2020) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12550 · Cited by 19

Neighbours and relatives: accounting for spatial distribution when testing causal hypotheses in cultural evolution

Bromham, L. D., & Yaxley, K. J. (2023) · Evolutionary Human Sciences · doi:10.1017/ehs.2023.23 · Cited by 18

[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