Iván Díaz

3.9k total citations
97 papers, 1.3k citations indexed

About

Iván Díaz is a scholar working on Statistics and Probability, Epidemiology and Economics and Econometrics. According to data from OpenAlex, Iván Díaz has authored 97 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Statistics and Probability, 25 papers in Epidemiology and 15 papers in Economics and Econometrics. Recurrent topics in Iván Díaz's work include Advanced Causal Inference Techniques (41 papers), Statistical Methods and Inference (25 papers) and Statistical Methods and Bayesian Inference (20 papers). Iván Díaz is often cited by papers focused on Advanced Causal Inference Techniques (41 papers), Statistical Methods and Inference (25 papers) and Statistical Methods and Bayesian Inference (20 papers). Iván Díaz collaborates with scholars based in United States, United Kingdom and Germany. Iván Díaz's co-authors include Mark J. van der Laan, Hooman Kamel, Babak B. Navi, Kara E. Rudolph, Costantino Iadecola, Alexander E. Merkler, Monika M. Safford, Xian Wu, Michael Rosenblum and Nima S. Hejazi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of the American Statistical Association and Journal of the American College of Cardiology.

In The Last Decade

Iván Díaz

88 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Iván Díaz United States 22 353 329 325 204 153 97 1.3k
Richard Wyss United States 17 408 1.2× 192 0.6× 218 0.7× 45 0.2× 337 2.2× 62 1.5k
Edouard L. Fu Sweden 23 91 0.3× 157 0.5× 460 1.4× 51 0.3× 133 0.9× 82 1.7k
Lily G. Bessette United States 14 135 0.4× 144 0.4× 256 0.8× 17 0.1× 240 1.6× 48 1.1k
Chava L. Ramspek Netherlands 16 57 0.2× 225 0.7× 209 0.6× 32 0.2× 108 0.7× 33 1.2k
Alison Bourke United Kingdom 9 67 0.2× 206 0.6× 139 0.4× 42 0.2× 134 0.9× 13 1.1k
Mireille E. Schnitzer Canada 16 256 0.7× 108 0.3× 227 0.7× 19 0.1× 124 0.8× 75 1.0k
Jason Nelson United States 20 77 0.2× 162 0.5× 130 0.4× 44 0.2× 130 0.8× 56 1.2k
Jennifer Christian United States 20 89 0.3× 209 0.6× 225 0.7× 28 0.1× 177 1.2× 61 1.2k
Lucinda Archer United Kingdom 13 62 0.2× 198 0.6× 170 0.5× 26 0.1× 129 0.8× 34 1.2k
Ryan Ferguson United States 17 65 0.2× 75 0.2× 179 0.6× 67 0.3× 129 0.8× 52 1.1k

Countries citing papers authored by Iván Díaz

Since Specialization
Citations

This map shows the geographic impact of Iván Díaz'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 Iván Díaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Iván Díaz more than expected).

Fields of papers citing papers by Iván Díaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Iván Díaz. 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 Iván Díaz. The network helps show where Iván Díaz may publish in the future.

Co-authorship network of co-authors of Iván Díaz

This figure shows the co-authorship network connecting the top 25 collaborators of Iván Díaz. A scholar is included among the top collaborators of Iván Díaz 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 Iván Díaz. Iván Díaz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Preiss, Alexander, Abhishek Bhatia, John M. Baratta, et al.. (2025). Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER consortia. PLoS Medicine. 22(9). e1004711–e1004711. 1 indexed citations
2.
Santacatterina, Michele, et al.. (2025). Identification and Estimation of Causal Effects Using Non‐Concurrent Controls in Platform Trials. Statistics in Medicine. 44(6). e70017–e70017.
3.
Díaz, Iván, Mario Geiger, & Richard McKinley. (2024). Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical data. PubMed Central. 2(May 2024). 834–855. 1 indexed citations
4.
5.
Gilbert, Peter B., James Peng, Larry Han, et al.. (2024). A surrogate endpoint-based provisional approval causal roadmap, illustrated by vaccine development. Biostatistics. 26(1).
6.
Rudolph, Kara E., et al.. (2023). Efficient and Flexible Estimation of Natural Direct and Indirect Effects under Intermediate Confounding and Monotonicity Constraints. Biometrics. 79(4). 3126–3139. 3 indexed citations
7.
Kamel, Hooman, Ava L. Liberman, Alexander E. Merkler, et al.. (2023). Validation of the International Classification of Diseases, Tenth Revision Code for the National Institutes of Health Stroke Scale Score. Circulation Cardiovascular Quality and Outcomes. 16(3). e009215–e009215. 12 indexed citations
8.
Díaz, Iván. (2023). Non-agency interventions for causal mediation in the presence of intermediate confounding. Journal of the Royal Statistical Society Series B (Statistical Methodology). 86(2). 435–460. 3 indexed citations
9.
Díaz, Iván, Nicholas J. Williams, & Kara E. Rudolph. (2023). Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders. SHILAP Revista de lepidopterología. 11(1). 4 indexed citations
10.
Hejazi, Nima S., Kara E. Rudolph, & Iván Díaz. (2022). medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R. The Journal of Open Source Software. 7(69). 3979–3979. 4 indexed citations
11.
Creber, Ruth Masterson, Brock Daniels, Kevin G. Munjal, et al.. (2022). Using Mobile Integrated Health and telehealth to support transitions of care among patients with heart failure (MIGHTy-Heart): protocol for a pragmatic randomised controlled trial. BMJ Open. 12(3). e054956–e054956. 9 indexed citations
12.
Kamel, Hooman, Neal S. Parikh, Abhinaba Chatterjee, et al.. (2021). Access to Mechanical Thrombectomy for Ischemic Stroke in the United States. Stroke. 52(8). 2554–2561. 41 indexed citations
13.
Benkeser, David, Iván Díaz, Alex Luedtke, et al.. (2020). Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes. Biometrics. 77(4). 1467–1481. 42 indexed citations
14.
Kamel, Hooman, Babak B. Navi, Neal S. Parikh, et al.. (2020). Machine Learning Prediction of Stroke Mechanism in Embolic Strokes of Undetermined Source. Stroke. 51(9). e203–e210. 36 indexed citations
15.
Hejazi, Nima S., Kara E. Rudolph, Mark J. van der Laan, & Iván Díaz. (2020). Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. arXiv (Cornell University). 7 indexed citations
16.
Díaz, Iván, Marco Carone, & Mark J. van der Laan. (2016). Second-Order Inference for the Mean of a Variable Missing at Random. The International Journal of Biostatistics. 12(1). 333–349. 5 indexed citations
17.
Díaz, Iván & Michael Rosenblum. (2015). Targeted Maximum Likelihood Estimation using Exponential Families. The International Journal of Biostatistics. 11(2). 233–251. 3 indexed citations
18.
Díaz, Iván & Mark J. van der Laan. (2013). Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems. The International Journal of Biostatistics. 9(2). 149–160. 29 indexed citations
19.
Cervantes, Víctor H., et al.. (2008). Intervalos de confianza e intervalos de credibilidad para una proporción. SHILAP Revista de lepidopterología. 8 indexed citations
20.
Díaz, Iván, et al.. (2000). Abordajes quirúrgicos en la artroplastía total de rodilla. 14(3). 275–278.

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.

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