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.
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
20171.3k citationsStefan Wager, Susan AtheyJournal of the American Statistical Associationprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Stefan Wager'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 Stefan Wager with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Wager more than expected).
This network shows the impact of papers produced by Stefan Wager. 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 Stefan Wager. The network helps show where Stefan Wager may publish in the future.
Co-authorship network of co-authors of Stefan Wager
This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Wager.
A scholar is included among the top collaborators of Stefan Wager 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 Stefan Wager. Stefan Wager is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ignatiadis, Nikolaos & Stefan Wager. (2019). Covariate-Powered Empirical Bayes Estimation. Neural Information Processing Systems. 32. 9620–9632.2 indexed citations
9.
Wager, Stefan & Susan Athey. (2017). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. 113(523). 1228–1242.1346 indexed citations breakdown →
10.
Hirshberg, David A. & Stefan Wager. (2017). Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities. arXiv (Cornell University).
11.
Wager, Stefan, et al.. (2017). Learning Objectives for Treatment Effect Estimation. arXiv (Cornell University).11 indexed citations
G’Sell, Max, Stefan Wager, Alexandra Chouldechova, & Robert Tibshirani. (2013). False Discovery Rate Control for Sequential Selection Procedures, with Application to the Lasso. arXiv (Cornell University).4 indexed citations
16.
Wager, Stefan, Trevor Hastie, & Bradley Efron. (2013). Standard Errors for Bagged Predictors and Random Forests. arXiv (Cornell University).3 indexed citations
Wager, Stefan, et al.. (2012). Cell range extension in LTE in-band relays: Analysis of radio link, subframe allocation and protocol perfornance of FTP traffic model. European Wireless Conference. 1–6.3 indexed citations
19.
Chakraborty, S.S. & Stefan Wager. (1995). INHIBIT SENSE MULTIPLE ACCESS WITH POLLING : A MEDIA ACCESS CONTROL SCHEME FOR THE MOBILE ENVIRONMENT , AND ITS ADAPTATION TO THE GSM GENERAL PACKET RADIO SERVICES. Asia-Pacific Conference on Communications. 926–930.3 indexed citations
20.
Chakraborty, S.S. & Stefan Wager. (1995). Inhibit Sense Multiple Access with Reservation, a Contender for GSM/GPRS Packet Services.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.