Niki Kilbertus

1.0k total citations · 1 hit paper
14 papers, 260 citations indexed

About

Niki Kilbertus is a scholar working on Artificial Intelligence, Molecular Biology and Safety Research. According to data from OpenAlex, Niki Kilbertus has authored 14 papers receiving a total of 260 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Molecular Biology and 2 papers in Safety Research. Recurrent topics in Niki Kilbertus's work include Metabolomics and Mass Spectrometry Studies (2 papers), Health Systems, Economic Evaluations, Quality of Life (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Niki Kilbertus is often cited by papers focused on Metabolomics and Mass Spectrometry Studies (2 papers), Health Systems, Economic Evaluations, Quality of Life (2 papers) and Explainable Artificial Intelligence (XAI) (2 papers). Niki Kilbertus collaborates with scholars based in Germany, United Kingdom and United States. Niki Kilbertus's co-authors include Bernhard Schölkopf, I. W. Harry, Stefan Feuerriegel, Mihaela van der Schaar, Alicia Curth, Isaac S. Kohane, Stefan Bauer, Giambattista Parascandolo, Mateo Rojas-Carulla and Adrian Weller and has published in prestigious journals such as Nature Medicine, Nature Cell Biology and Scientific Reports.

In The Last Decade

Niki Kilbertus

10 papers receiving 255 citations

Hit Papers

Causal machine learning for predicting treatment outcomes 2024 2026 2025 2024 25 50 75

Peers

Niki Kilbertus
Comparison fields: 5 of 96
  • Artificial Intelligence 102
  • Astronomy and Astrophysics 85
  • Geophysics 23
  • Molecular Biology 16
  • Safety Research 16
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Citations per field, relative to Niki Kilbertus
Niki Kilbertus · 1×
Citations per year, relative to Niki Kilbertus
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Countries citing papers authored by Niki Kilbertus

Since Specialization
Citations

This map shows the geographic impact of Niki Kilbertus'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 Niki Kilbertus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niki Kilbertus more than expected).

Fields of papers citing papers by Niki Kilbertus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Niki Kilbertus. 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 Niki Kilbertus. The network helps show where Niki Kilbertus may publish in the future.

Co-authorship network of co-authors of Niki Kilbertus

This figure shows the co-authorship network connecting the top 25 collaborators of Niki Kilbertus. A scholar is included among the top collaborators of Niki Kilbertus based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Niki Kilbertus. Niki Kilbertus is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
# Work Indexed citations
1 0
2 0
3 0
4 0
5 1
6
Causal machine learning for predicting treatment outcomes breakdown →
95
7 5
8 16
9
On Disentangled Representations Learned from Correlated Data
6
10
Is Independence all you need? On the Generalization of Representations Learned from Correlated Data
1
11
Fair Decisions Despite Imperfect Predictions
8
12 93
13
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
8
14
Learning Independent Causal Mechanisms
27

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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