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Topic · Artificial Intelligence

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. 22.5% 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%
4.9k
per year above
Top-10% rate
22.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+0%
the tick is no change

Which countries lead Bayesian Modeling and Causal Inference research?

By volume, the United States and China publish the most (712 and 611 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 712 works
  2. 2 China 611 works
  3. 3 Germany 204 works
  4. 4 United Kingdom 183 works
  5. 5 France 138 works
  6. 6 Italy 137 works
  7. 7 Canada 90 works
  8. 8 India 90 works
  9. 9 Japan 82 works
  10. 10 Spain 82 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: 30.6%China: 26.2%Germany: 8.7%United Kingdom: 7.9%6 others listed: 26.6%31%largest
United States712 · 30.6%China611 · 26.2%Germany204 · 8.7%United Kingdom183 · 7.9%6 others listed619 · 26.6%

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

Which institutions lead Bayesian Modeling and Causal Inference research?

By volume in 2022–2025, 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, 2022–2025

  1. 1 Centre National de la Recherche ScientifiqueFrance 19 works
  2. 2 University of California, Los AngelesUnited States 19 works
  3. 3 Northwestern Polytechnical UniversityChina 18 works
  4. 4 University of EdinburghUnited Kingdom 18 works
  5. 5 National University of Defense TechnologyChina 16 works
  6. 6 National University of SingaporeSingapore 16 works
  7. 7 Carnegie Mellon UniversityUnited States 16 works
  8. 8 Ludwig-Maximilians-Universität MünchenGermany 16 works
  9. 9 Hefei University of TechnologyChina 14 works
  10. 10 University of OxfordUnited Kingdom 13 works

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

The most-cited researchers publishing on Bayesian Modeling and Causal Inference include Amos Tversky, Yoshua Bengio and Alex Pentland.

  1. 1 Amos Tversky United States 19k citations
  2. 2 Yoshua Bengio Canada 17k citations
  3. 3 Alex Pentland United States 6k citations

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

Where is Bayesian Modeling and Causal Inference research done?

The largest centres of Bayesian Modeling and Causal Inference research in 2022–2025 are Beijing (China), London (United Kingdom), Paris (France) and Shanghai (China).

Largest cities, 2022–2025

  1. 1 Beijing China 114 works
  2. 2 London United Kingdom 49 works
  3. 3 Paris France 48 works
  4. 4 Shanghai China 44 works
  5. 5 Xi'an China 40 works
  6. 6 Tokyo Japan 38 works
  7. 7 Hefei China 37 works
  8. 8 Chengdu China 35 works
  9. 9 Munich Germany 33 works
  10. 10 New York United States 33 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, University of Edinburgh and Sichuan University 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 24.73%fractional works in this node (log) →share in the world top 10% →University of California, Los Angeles: 18, 38.1%University of Edinburgh: 18, 20.5%Sichuan University: 13, 44.0%Northwestern Polytechnical University: 18, 25.4%Carnegie Mellon University: 16, 10.4%Anhui University: 10, 37.3%National University of Singapore: 16, 17.8%Ludwig-Maximilians-Universität München: 16, 13.7%TU Wien: 10, 16.3%Hefei University of Technology: 14, 23.8%Sichuan UniversityUniversity of Califo…Northwestern Polytec…University of Edinbu…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 University of California, Los AngelesUnited States 63.538.1%6.7×18 -34.7%
2 University of EdinburghUnited Kingdom 63.420.5%7.9×18 +73.7%
3 Sichuan UniversityChina 60.044.0%2.7×13 +261.7%
4 Northwestern Polytechnical UniversityChina 57.125.4%5.3×18 +96.3%
5 Carnegie Mellon UniversityUnited States 55.110.4%12.9×16 +31.6%
6 Anhui UniversityChina 53.737.3%7.4×10 —
7 National University of SingaporeSingapore 53.017.8%5.7×16 +10.3%
8 Ludwig-Maximilians-Universität MünchenGermany 52.513.7%8.4×16 0.0%
9 TU WienAustria 50.716.3%11.1×10 +14.9%
10 Hefei University of TechnologyChina 48.523.8%8.2×14 -45.6%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Bayesian Modeling and Causal Inference research growing?

Output in 2018–2022 was 0% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Bayesian Modeling and Causal Inference.

19801990200020102020
grewheldshrank

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

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.