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.
The Prevention and Treatment of Missing Data in Clinical Trials
Countries citing papers authored by Susan A. Murphy
Since
Specialization
Citations
This map shows the geographic impact of Susan A. Murphy'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 Susan A. Murphy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Susan A. Murphy more than expected).
This network shows the impact of papers produced by Susan A. Murphy. 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 Susan A. Murphy. The network helps show where Susan A. Murphy may publish in the future.
Co-authorship network of co-authors of Susan A. Murphy
This figure shows the co-authorship network connecting the top 25 collaborators of Susan A. Murphy.
A scholar is included among the top collaborators of Susan A. Murphy 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 Susan A. Murphy. Susan A. Murphy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zhang, Kelly, Lucas Janson, & Susan A. Murphy. (2021). Statistical Inference with M-Estimators on Bandit Data.. arXiv (Cornell University).1 indexed citations
Qian, Tianchen, Predrag Klasnja, & Susan A. Murphy. (2019). Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health study. arXiv (Cornell University).3 indexed citations
10.
Fahrenbach, John, et al.. (2018). Transforming care through bedside leader rounding: Use of handheld technology leads to improvement in perceived patient satisfaction. SHILAP Revista de lepidopterología.1 indexed citations
Klasnja, Predrag, Eric B. Hekler, Saul Shiffman, et al.. (2015). Microrandomized trials: An experimental design for developing just-in-time adaptive interventions.. Health Psychology. 34(Suppl). 1220–1228.388 indexed citations breakdown →
14.
Fonteneau, Raphaël, Susan A. Murphy, Louis Wehenkel, & Damien Ernst. (2010). Computing bounds for kernel-based policy evaluation in reinforcement learning. Open Repository and Bibliography (University of Liège).2 indexed citations
15.
Lizotte, Daniel J., Michael Bowling, & Susan A. Murphy. (2010). Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis. International Conference on Machine Learning. 695–702.37 indexed citations
16.
Fonteneau, Raphaël, Susan A. Murphy, Louis Wehenkel, & Damien Ernst. (2010). Model-Free Monte Carlo-like Policy Evaluation. Journal of Machine Learning Research. 9. 217–224.6 indexed citations
17.
Fonteneau, Raphaël, Susan A. Murphy, Louis Wehenkel, & Damien Ernst. (2009). Dynamic treatment regimes using reinforcement learning: a cautious generalization approach. Open Repository and Bibliography (University of Liège).1 indexed citations
18.
Murphy, Susan A., Aad van der Vaart, & Jon A. Wellner. (1999). Current Status Regression. Mathematical Methods of Statistics. 8. 407–425.15 indexed citations
19.
Murphy, Susan A. & Aad van der Vaart. (1998). Observed Information in Semiparametric Models. Deep Blue (University of Michigan).1 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.