Kosuke Imai
Impact in
- Statistics and Probability top 0.02%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
- Statistical Methods in Clinical Trials
- Economics and Econometrics top 0.1%
- Health Systems, Economic Evaluations, Quality of Life
Papers in
-
- Advanced Causal Inference Techniques 61
- Statistical Methods and Inference 41
- Statistical Methods and Bayesian Inference 37
- Statistical Methods in Clinical Trials 12
- Survey Sampling and Estimation Techniques 10
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- Electoral Systems and Political Participation 20
- Co-authors
- Luke J Keele (8 shared papers)Dustin H. Tingley (11 shared papers)Gary King (12 shared papers)Daniel E. Ho (6 shared papers)Elizabeth A. Stuart (5 shared papers)Teppei Yamamoto (12 shared papers)Marc Ratkovic (3 shared papers)Kentaro Hirose (2 shared papers)
- Journals
- Political Analysis (16 papers)Journal of the American Statistical Association (14 papers)American Journal of Political Science (9 papers)American Political Science Review (8 papers)Journal of the Royal Statistical Society Series A (Statistics in Society) (5 papers)
- Partner nations
- United StatesJapanChina
In The Last Decade
Kosuke Imai
126 papers receiving 23.2k citations
Kosuke Imai's Hit Papers
Peers
Comparison fields: 5 of 223
- Statistics and Probability 4.4k
- Economics and Econometrics 3.2k
- Political Science and International Relations 2.8k
- Sociology and Political Science 5.0k
- Safety Research 898
Countries citing papers authored by Kosuke Imai
This map shows the geographic impact of Kosuke Imai'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 Kosuke Imai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kosuke Imai more than expected).
Fields of papers citing papers by Kosuke Imai
This network shows the impact of papers produced by Kosuke Imai. 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 Kosuke Imai. The network helps show where Kosuke Imai may publish in the future.
Co-authors
The 25 scholars most cited alongside Kosuke Imai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 135 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | MatchIt : Nonparametric Preprocessing for Parametric Causal Inference Hit paper breakdown → | 2011 | 3338 |
| 2 | Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference Hit paper breakdown → | 2007 | 3074 |
| 3 | mediation : R Package for Causal Mediation Analysis Hit paper breakdown → | 2014 | 2960 |
| 4 | A general approach to causal mediation analysis. Hit paper breakdown → | 2010 | 2748 |
| 5 | Redefine statistical significance Hit paper breakdown → | 2017 | 1909 |
| 6 | Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies Hit paper breakdown → | 2011 | 1043 |
| 7 | Covariate Balancing Propensity Score Hit paper breakdown → | 2013 | 860 |
| 8 | Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects Hit paper breakdown → | 2008 | 705 |
| 9 | 2004 | 589 | |
| 10 | Statistical Analysis of List Experiments Hit paper breakdown → | 2012 | 383 |
| 11 | On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data Hit paper breakdown → | 2020 | 365 |
| 12 | 2013 | 306 | |
| 13 | 2012 | 285 | |
| 14 | Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments Hit paper breakdown → | 2013 | 278 |
| 15 | Zelig: Everyone's Statistical Software | 2006 | 260 |
| 16 | 2008 | 254 | |
| 17 | 2013 | 251 | |
| 18 | 2009 | 228 | |
| 19 | When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? Hit paper breakdown → | 2019 | 226 |
| 20 | 2018 | 208 |
About Kosuke Imai
Kosuke Imai is a scholar working on Statistics and Probability, Political Science and International Relations, Management Science and Operations Research, Artificial Intelligence and Sociology and Political Science, having authored 135 papers that have together received 24.3k indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (61 papers), Statistical Methods and Inference (41 papers), Statistical Methods and Bayesian Inference (37 papers), Electoral Systems and Political Participation (20 papers), Statistical Methods in Clinical Trials (12 papers), Survey Sampling and Estimation Techniques (10 papers), Political Conflict and Governance (9 papers) and Qualitative Comparative Analysis Research (9 papers). The work is most often cited by research in Statistics and Probability (4.4k citations), Economics and Econometrics (3.2k citations), Political Science and International Relations (2.8k citations), Sociology and Political Science (5.0k citations) and Safety Research (898 citations). Kosuke Imai has collaborated with scholars based in United States, Japan and China. Frequent co-authors include Luke J Keele, Dustin H. Tingley, Gary King, Daniel E. Ho, Elizabeth A. Stuart, Teppei Yamamoto, Marc Ratkovic, Kentaro Hirose, David A. van Dyk and In Song Kim. Their work appears in journals such as Political Analysis, Journal of the American Statistical Association, American Journal of Political Science, American Political Science Review and Journal of the Royal Statistical Society Series A (Statistics in Society).
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.