Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
2004696 citationsAlison Gopnik, Clark Glymour et al.profile →
This map shows the geographic impact of David Danks'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 David Danks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Danks more than expected).
This network shows the impact of papers produced by David Danks. 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 David Danks. The network helps show where David Danks may publish in the future.
Co-authorship network of co-authors of David Danks
This figure shows the co-authorship network connecting the top 25 collaborators of David Danks.
A scholar is included among the top collaborators of David Danks 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 David Danks. David Danks is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Danks, David. (2013). Moving from Levels & Reduction to Dimensions & Constraints. Cognitive Science. 35(35). 2124.3 indexed citations
15.
Danks, David, Clark Glymour, & Robert E. Tillman. (2008). Integrating Locally Learned Causal Structures with Overlapping Variables. Neural Information Processing Systems. 21. 1665–1672.37 indexed citations
16.
Danks, David, et al.. (2007). Decision Making Using Learned Causal Structures. eScholarship (California Digital Library). 29(29).3 indexed citations
17.
Danks, David, et al.. (2007). Task Influences on Category Learning. eScholarship (California Digital Library). 29(29).1 indexed citations
18.
Danks, David & Samantha L. Schwartz. (2006). Effects of Causal Strength on Learning from Biased Sequences. eScholarship (California Digital Library). 28(28).6 indexed citations
19.
Danks, David & Samantha L. Schwartz. (2005). Causal Learning from Biased Sequences. eScholarship (California Digital Library). 27(27).5 indexed citations
20.
Kushnir, Tamar, Alison Gopnik, Laura Schulz, & David Danks. (2003). Inferring Hidden Causes. eScholarship (California Digital Library). 25(25).25 indexed citations
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.