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.
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
20073.1k citationsDaniel E. Ho, Kosuke Imai et al.profile →
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
20113.0k citationsDaniel E. Ho, Kosuke Imai et al.profile →
Advances, challenges and opportunities in creating data for trustworthy AI
2022283 citationsDaniel E. Ho et al.Nature Machine Intelligenceprofile →
How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
This map shows the geographic impact of Daniel E. Ho'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 E. Ho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel E. Ho more than expected).
This network shows the impact of papers produced by Daniel E. Ho. 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 E. Ho. The network helps show where Daniel E. Ho may publish in the future.
Co-authorship network of co-authors of Daniel E. Ho
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel E. Ho.
A scholar is included among the top collaborators of Daniel E. Ho 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 Daniel E. Ho. Daniel E. Ho is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ho, Daniel E., et al.. (2020). Due Process and Mass Adjudication: Crisis and Reform. Stanford Law Review. 72(1). 1.3 indexed citations
11.
Engstrom, David Freeman & Daniel E. Ho. (2020). Algorithmic Accountability in the Administrative State. Yale journal on regulation. 37(3). 1.27 indexed citations
12.
Ho, Daniel E.. (2016). Does Peer Review Work? An Experiment of Experimentalism. Stanford Law Review. 69(1). 1.17 indexed citations
13.
Ho, Daniel E.. (2014). Measuring Agency Preferences: Experts, Voting, and the Power of Chairs. The De Paul law review. 59(2). 333.3 indexed citations
Ho, Daniel E.. (2012). Fudging the Nudge: Information Disclosure and Restaurant Grading. The Yale Law Journal. 122(3). 2.39 indexed citations
16.
Ho, Daniel E., et al.. (2010). Did Liberal Justices Invent the Standing Doctrine? an Empirical Study of the Evolution of Standing, 1921-2006. Stanford Law Review. 62(3). 591.5 indexed citations
17.
Ho, Daniel E. & Kevin M. Quinn. (2008). Viewpoint Diversity and Media Consolidation: An Empirical Study. Stanford Law Review. 61(4). 781.19 indexed citations
18.
Ho, Daniel E.. (2005). Affirmative Action's Affirmative Actions: A Reply to Sander. The Yale Law Journal. 114(8). 2011.6 indexed citations
19.
Ho, Daniel E.. (2005). Why Affirmative Action Does Not Cause Black Students To Fail the Bar. The Yale Law Journal. 114(8). 1997.27 indexed citations
20.
Epstein, Lee, et al.. (2005). The Supreme Court During Crisis: How War Affects only Non-War Cases. Digital Access to Scholarship at Harvard (DASH) (Harvard University). 80.54 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.