Elements of Causal Inference: Foundations and Learning Algorithms

358 indexed citations

Abstract

loading...

About

This paper, published in 2017, received 358 indexed citations. Written by Jonas Peters, Dominik Janzing and Bernhard Schölkopf covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (209 citations), Statistics and Probability (53 citations) and Management Science and Operations Research (47 citations). Published in OAPEN (OAPEN).

In The Last Decade

doi.org/w91075987 →

Countries where authors are citing Elements of Causal Inference: Foundations and Learning Algorithms

Specialization
Citations

This map shows the geographic impact of Elements of Causal Inference: Foundations and Learning Algorithms. 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 Elements of Causal Inference: Foundations and Learning Algorithms with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elements of Causal Inference: Foundations and Learning Algorithms more than expected).

Fields of papers citing Elements of Causal Inference: Foundations and Learning Algorithms

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Elements of Causal Inference: Foundations and Learning Algorithms. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Elements of Causal Inference: Foundations and Learning Algorithms.

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.

This paper is also available at doi.org/w91075987.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026