Most cited papers in Bayesian Modeling and Causal Inference
What are the most cited papers in Bayesian Modeling and Causal Inference?
The most cited paper in Bayesian Modeling and Causal Inference is "Prospect theory: An analysis of decision under risk" (Daniel J. Kahneman, Amos Tversky, 1988), with 32,957 citations. Next come "Advances in prospect theory: Cumulative representation of uncertainty" (15,858) and "ANALYZING TABLES OF STATISTICAL TESTS" (13,907).
Showing 50 of 98 papers. Every paper with more than 1,626 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Prospect theory: An analysis of decision under risk Daniel J. Kahneman, Amos Tversky · Cambridge University Press eBooks | 25 Mar 1988 | 32,957 |
| 2 | Advances in prospect theory: Cumulative representation of uncertainty Amos Tversky, Daniel J. Kahneman · Journal of Risk and Uncertainty | Oct 1992 | 15,858 |
| 3 | ANALYZING TABLES OF STATISTICAL TESTS William R. Rice · Evolution | 1989 | 13,907 |
| 4 | On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper) Gauriat, Charles-Maxime, Pencolé, Yannick, Ribot, Pauline et al. · OASIcs : OpenAccess Series in Informatics | 2024 | 13,571 |
| 5 | From local explanations to global understanding with explainable AI for trees Scott Lundberg, Gabriel Erion, Hugh Chen et al. · Nature Machine Intelligence | 17 Jan 2020 | 10,292 |
| 6 | A Unified Approach to Interpreting Model Predictions Scott Lundberg, Su‐In Lee · arXiv (Cornell University) | 22 May 2017 | 7,625 |
| 7 | Stan : A Probabilistic Programming Language Bob Carpenter, Andrew Gelman, Matthew D. Hoffman et al. · Journal of Statistical Software | 2017 | 7,589 |
| 8 | In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings Herbert W. Marsh, Kit‐Tai Hau, Zhonglin Wen · Structural Equation Modeling A Multidisciplinary Journal | 2 Jun 2004 | 6,674 |
| 9 | Factor graphs and the sum-product algorithm Frank R. Kschischang, Brendan J. Frey, Hans‐Andrea Loeliger · IEEE Transactions on Information Theory | 2001 | 6,555 |
| 10 | Multitask Learning Rich Caruana · Machine Learning | Jul 1997 | 6,547 |
| 11 | Probabilistic graphical models : principles and techniques Daniel L. Koller, Nir Friedman | 31 Jul 2009 | 6,429 |
| 12 | Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses Quang Vuong · Econometrica | Mar 1989 | 6,074 |
| 13 | A perspective on judgment and choice: Mapping bounded rationality. Daniel J. Kahneman · American Psychologist | 2003 | 5,582 |
| 14 | WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility David J. Lunn, Andrew C. Thomas, Nicky Best et al. · Statistics and Computing | Oct 2000 | 5,419 |
| 15 | Modeling Relational Data with Graph Convolutional Networks Michael Schlichtkrull, Thomas Kipf, Peter Bloem et al. · Lecture notes in computer science | 2018 | 5,280 |
| 16 | Divergence measures based on the Shannon entropy Jinfeng Lin · IEEE Transactions on Information Theory | 1991 | 5,278 |
| 17 | A survey of methods for explaining black box models Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri et al. · ISTI Open Portal | 2019 | 5,066 |
| 18 | Training Products of Experts by Minimizing Contrastive Divergence Geoffrey E. Hinton · Neural Computation | Aug 2002 | 5,049 |
| 19 | Subjective probability: A judgment of representativeness Daniel J. Kahneman, Amos Tversky · Cognitive Psychology | Jul 1972 | 4,923 |
| 20 | Bayesian Network Classifiers Nir Friedman, Dan Geiger, Moisés Goldszmidt · Machine Learning | Nov 1997 | 4,769 |
| 21 | Multiple Comparisons Using Rank Sums Olive Jean Dunn · Technometrics | Aug 1964 | 4,644 |
| 22 | MatchIt : Nonparametric Preprocessing for Parametric Causal Inference Daniel E. Ho, Kosuke Imai, Gary King et al. · Journal of Statistical Software | 2011 | 4,491 |
| 23 | Best-worst multi-criteria decision-making method Jafar Rezaei · Omega | 3 Dec 2014 | 4,312 |
| 24 | Maxmin expected utility with non-unique prior Itzhak Gilboa, David Schmeidler · Journal of Mathematical Economics | 1989 | 4,217 |
| 25 | Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Amos Tversky, Daniel J. Kahneman · Psychological Review | Oct 1983 | 4,013 |
| 26 | Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems Steffen Lilholt Lauritzen, David J. Spiegelhalter · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1988 | 3,984 |
| 27 | Permutation inference for the general linear model Anderson M. Winkler, Gerard R. Ridgway, Matthew Webster et al. · NeuroImage | 11 Feb 2014 | 3,891 |
| 28 | An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola et al. · Machine Learning | Nov 1999 | 3,812 |
| 29 | Reaching a Consensus Morris H. DeGroot · Journal of the American Statistical Association | Mar 1974 | 3,609 |
| 30 | Reasoning the fast and frugal way: Models of bounded rationality. Gerd Gigerenzer, Daniel G. Goldstein · Psychological Review | Oct 1996 | 3,536 |
| 31 | A Bayesian Method for the Induction of Probabilistic Networks from Data Gregory F. Cooper, Edward H. Herskovits · Machine Learning | Oct 1992 | 3,533 |
| 32 | Detecting Novel Associations in Large Data Sets David N. Reshef, Yakir Reshef, Hilary K. Finucane et al. · Science | 15 Dec 2011 | 3,453 |
| 33 | Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models T. Florian Jaeger · Journal of Memory and Language | 9 Feb 2008 | 3,386 |
| 34 | A few useful things to know about machine learning Pedro Domingos · Communications of the ACM | 25 Sep 2012 | 3,368 |
| 35 | Using Bayesian Networks to Analyze Expression Data Nir Friedman, Michal Linial, Iftach Nachman et al. · Journal of Computational Biology | Aug 2000 | 3,337 |
| 36 | Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David E. Heckerman, Dan Geiger, David M. Chickering · Machine Learning | Sep 1995 | 3,276 |
| 37 | A theory of anticipated utility John C. Quiggin · Journal of Economic Behavior & Organization | Dec 1982 | 3,224 |
| 38 | A comparison of event models for naive bayes text classification Andrew McCallum, Kamal Nigam | 1998 | 3,217 |
| 39 | The Probabilistic Relevance Framework: BM25 and Beyond Stephen E. Robertson, Hugo Zaragoza · Foundations and Trends® in Information Retrieval | 17 Sep 2009 | 3,198 |
| 40 | On the Optimality of the Simple Bayesian Classifier under Zero-One Loss Pedro Domingos, Michael J. Pazzani · Machine Learning | Nov 1997 | 3,111 |
| 41 | Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties Joel Schwartz · Epidemiology | 1999 | 3,075 |
| 42 | Theoretical and Empirical Analysis of ReliefF and RReliefF Marko Robnik‐Šikonja, Igor Kononenko · Machine Learning | 23 Sep 2003 | 3,031 |
| 43 | A Practical Bayesian Framework for Backpropagation Networks David Mackay · Neural Computation | May 1992 | 2,977 |
| 44 | Estimating Continuous Distributions in Bayesian Classifiers George H. John, Pat Langley · arXiv (Cornell University) | 20 Feb 2013 | 2,923 |
| 45 | AIC model selection using Akaike weights Eric‐Jan Wagenmakers, Simon Farrell · Psychonomic Bulletin & Review | Feb 2004 | 2,840 |
| 46 | Learning to rank using gradient descent Chris J. C. Burges, Tal Shaked, Erin Renshaw et al. | 2005 | 2,812 |
| 47 | Markov logic networks Matthew Richardson, Pedro Domingos · Machine Learning | 27 Jan 2006 | 2,687 |
| 48 | Probabilistic programming in Python using PyMC3 John Salvatier, Thomas V. Wiecki, Christopher Fonnesbeck · PeerJ Computer Science | 6 Apr 2016 | 2,578 |
| 49 | High-dimensional graphs and variable selection with the Lasso Nicolai Meinshausen, Peter Bühlmann · The Annals of Statistics | Jun 2006 | 2,560 |
| 50 | An empirical study of the naive Bayes classifier Irina Rish | 2001 | 2,542 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.