Daniel Malinsky

759 citations
23 papers · 302 · h-index 12

Impact in

Papers in

Daniel Malinsky

21 papers receiving 293 citations

Peers

Daniel Malinsky
Comparison fields: 5 of 93
  • Statistics and Probability 40
  • Health Informatics 5
  • History and Philosophy of Science 16
  • Transplantation 7
  • Hepatology 19
Replace Sophia Crüwell with:
Sophia Crüwell Germany
Bernard Robertson New Zealand
Anna Gottard Italy
Ravi Shroff United States
Jodi M. Casabianca United States
Rosaura Fernández-Pascual Spain
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Malinsky

Since Specialization
Citations

This map shows the geographic impact of Daniel Malinsky's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Malinsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Malinsky more than expected).

Fields of papers citing papers by Daniel Malinsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Malinsky. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Malinsky. The network helps show where Daniel Malinsky may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Malinsky, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Malinsky Line = papers co-authored together Daniel Malinsky links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201771
2 201564
3 202226
4 201718
5
Causal Structure Learning from Multivariate Time Series in Settings with Unmeasured Confounding.
201817
6 201913
7 202313
8 202211
9
Causal Learning for Partially Observed Stochastic Dynamical Systems
201811
10 202311
11 202211
12 201711
13 20255
14
Learning Optimal Fair Policies.
20194
15 20233
16 20253
17
Estimating Causal Effects with Ancestral Graph Markov Models.
20163
18 20242
19 20152
20
Learning the Structure of a Nonstationary Vector Autoregression.
20192

About Daniel Malinsky

Daniel Malinsky is a scholar working on Artificial Intelligence, Statistics and Probability, General Health Professions, History and Philosophy of Science and Clinical Psychology, having authored 23 papers that have together received 302 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (7 papers), Advanced Causal Inference Techniques (3 papers), Machine Learning and Algorithms (2 papers), Liver Disease and Transplantation (2 papers), Artificial Intelligence in Healthcare and Education (2 papers), Fault Detection and Control Systems (2 papers), Philosophy and History of Science (2 papers) and Agricultural risk and resilience (1 paper). The work is most often cited by research in Statistics and Probability (40 citations), Health Informatics (5 citations), History and Philosophy of Science (16 citations), Transplantation (7 citations) and Hepatology (19 citations). Daniel Malinsky has collaborated with scholars based in United States, Canada and Switzerland. Frequent co-authors include David Danks, Liam Kofi Bright, Peter Spirtes, Ilya Shpitser, Eric J. Tchetgen Tchetgen, Niels Richard Hansen, Andrew M. Cameron, Tanjala S. Purnell, James P. Hamilton and Dorry L. Segev. Their work appears in journals such as Philosophy of Science, Journal of the American Medical Informatics Association, Philosophy Compass, International Journal of Approximate Reasoning and Science Advances.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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