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Bayesian Modeling and Causal Inference

Bayesian Modeling and Causal Inference is a research topic within Artificial Intelligence. Science Explorer counts 22k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the learning, inference, and applications of Bayesian networks and related probabilistic graphical models. It covers topics such as causal inference, graphical model structure learning, Markov logic networks, and the use of imprecise probabilities in modeling. The papers also discuss various algorithms for probabilistic learning and highlight the applications of Bayesian networks in diverse fields such as ecology, healthcare, and decision making under uncertainty.

  • Bayesian Networks
  • Causal Inference
  • Graphical Models
  • Probabilistic Learning
  • Markov Logic Networks
  • Inference Algorithms
  • Causal Discovery
  • Probabilistic Graphical Models
  • Structure Learning
  • Imprecise Probabilities
Research works
22k
fractional, since 1950
In the world top 10%
2.2k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+6%
the tick is no change
Largest producer
United States
30% of the 10 largest, 2022-now
Bayesian Modeling and Causal Inference

Which countries lead Bayesian Modeling and Causal Inference research?

By volume, the United States and China publish the most (827 and 778 works in 2022-now).

By volume, 2022-now

  1. 1 United States 827 works
  2. 2 China 778 works
  3. 3 Germany 231 works
  4. 4 United Kingdom 210 works
  5. 5 France 153 works
  6. 6 Italy 150 works
  7. 7 India 114 works
  8. 8 Canada 105 works
  9. 9 Japan 99 works
  10. 10 Spain 90 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 30.0%China: 28.2%Germany: 8.4%United Kingdom: 7.6%6 others listed: 25.8%30%largest
United States827 · 30.0%China778 · 28.2%Germany231 · 8.4%United Kingdom210 · 7.6%6 others listed711 · 25.8%

Shares of the rows listed above, not of the whole node.

Which institutions lead Bayesian Modeling and Causal Inference research?

By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Bayesian Modeling and Causal Inference research, followed by University of California, Los Angeles and Northwestern Polytechnical University.

By volume, 2022-now

  1. 1 Centre National de la Recherche ScientifiqueFrance 50 works
  2. 2 University of California, Los AngelesUnited States 20 works
  3. 3 Northwestern Polytechnical UniversityChina 20 works
  4. 4 University of EdinburghUnited Kingdom 20 works
  5. 5 Ludwig-Maximilians-Universität MünchenGermany 20 works
  6. 6 National University of Defense TechnologyChina 19 works
  7. 7 Carnegie Mellon UniversityUnited States 18 works
  8. 8 National University of SingaporeSingapore 18 works
  9. 9 Chinese Academy of SciencesChina 18 works
  10. 10 University of OxfordUnited Kingdom 16 works

Who are the leading researchers in Bayesian Modeling and Causal Inference?

The most-cited researchers publishing on Bayesian Modeling and Causal Inference include Yoshua Bengio, Geoffrey E. Hinton and Robert John Tibshirani.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Robert John Tibshirani United States 396k citations
  4. 4 Daniel J. Kahneman United States 395k citations
  5. 5 Steven L. Salzberg United States 372k citations
  6. 6 Amos Tversky United States 327k citations
  7. 7 Karl John Friston United Kingdom 310k citations
  8. 8 Donald B. Rubin United States 301k citations
  9. 9 Trevor Hastie United States 301k citations
  10. 10 Xiaogang Wang China 260k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

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).

Most cited papers
#PaperReleasedCitations
1 Prospect theory: An analysis of decision under risk Daniel J. Kahneman, Amos Tversky · Cambridge University Press eBooks Book chapter 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 Conference paperOpen access 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 Open access 17 Jan 2020 10,292
All 98 most cited papers, with filters and sorting

Where is Bayesian Modeling and Causal Inference research done?

The largest centres of Bayesian Modeling and Causal Inference research in 2022-now are Beijing (China), London (United Kingdom), Shanghai (China) and Paris (France).

Largest cities, 2022-now

  1. 1 BeijingChina 138 works
  2. 2 LondonUnited Kingdom 57 works
  3. 3 ShanghaiChina 55 works
  4. 4 ParisFrance 52 works
  5. 5 Xi'anChina 46 works
  6. 6 TokyoJapan 45 works
  7. 7 HefeiChina 42 works
  8. 8 NanjingChina 42 works
  9. 9 ChengduChina 39 works
  10. 10 New YorkUnited States 37 works
See Bayesian Modeling and Causal Inference on the map

Where is the best place to study Bayesian Modeling and Causal Inference?

Among universities, judged by research, University of California, Los Angeles, Sichuan University and National University of Singapore score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 21.37%fractional works in this field (log) →share of work in the world top 10% →Chongqing UniversitySichuan UniversityUniversity of Califo…National University …
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1University of California, Los AngelesUnited States 74.227.8%5.7×20 +6.0%
2Sichuan UniversityChina 65.934.4%2.2×15 -25.9%
3National University of SingaporeSingapore 64.710.7%4.9×18 +70.2%
4Chongqing UniversityChina 62.337.5%3.2×11 +107.3%
5University of California, BerkeleyUnited States 62.218.0%5.0×15 +43.5%
6University of EdinburghUnited Kingdom 62.06.8%7.3×20 +85.1%
7Northwestern Polytechnical UniversityChina 61.321.0%5.6×20 -10.0%
8University of Science and Technology of ChinaChina 58.528.8%3.3×12 +140.8%
9Hefei University of TechnologyChina 57.916.7%9.0×15 +78.3%
10National University of Defense TechnologyChina 57.712.0%8.1×19 +60.3%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Bayesian Modeling and Causal Inference, country by country

Is Bayesian Modeling and Causal Inference research growing?

Output in 2022-2025 was 6% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Bayesian Modeling and Causal Inference.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Bayesian Modeling and Causal Inference as a percentage of every work published that year, 0.014% in 2024.

still counting0.020%0.040%Bayesian Modeling and Causal Inference0.016% in 202519801990200020102020

Fractional works of Bayesian Modeling and Causal Inference over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Bayesian Modeling and Causal Inference10.2% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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