| Mediation: R package for causal mediation analysis D Tingley, T Yamamoto, K Hirose, L Keele, K Imai UCLA Statistics/American Statistical Association, 2014 | 3768 | 2014 |
| Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments J Hainmueller, DJ Hopkins, T Yamamoto Political analysis 22 (1), 1-30, 2014 | 2133* | 2014 |
| Identification, inference and sensitivity analysis for causal mediation effects K Imai, L Keele, T Yamamoto Statistical Science 25 (1), 51-71, 2010 | 1978 | 2010 |
| Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies K Imai, L Keele, D Tingley, T Yamamoto American Political Science Review 105 (4), 2011 | 1856 | 2011 |
| Validating vignette and conjoint survey experiments against real-world behavior J Hainmueller, D Hangartner, T Yamamoto Proceedings of the National Academy of Sciences 112 (8), 2395-2400, 2015 | 1312 | 2015 |
| Experimental designs for identifying causal mechanisms K Imai, D Tingley, T Yamamoto Journal of the Royal Statistical Society Series A: Statistics in Society 176 …, 2013 | 538 | 2013 |
| Identification and sensitivity analysis for multiple causal mechanisms: Revisiting evidence from framing experiments K Imai, T Yamamoto Political Analysis 21 (2), 141-171, 2013 | 469 | 2013 |
| Causal mediation analysis using R K Imai, L Keele, D Tingley, T Yamamoto Advances in social science research using R, 129-154, 2010 | 385* | 2010 |
| The number of choice tasks and survey satisficing in conjoint experiments K Bansak, J Hainmueller, DJ Hopkins, T Yamamoto Political Analysis 26 (1), 112-119, 2018 | 252 | 2018 |
| Does conjoint analysis mitigate social desirability bias? Y Horiuchi, Z Markovich, T Yamamoto Political Analysis 30 (4), 535-549, 2022 | 237 | 2022 |
| Conjoint survey experiments K Bansak, J Hainmueller, DJ Hopkins, T Yamamoto, JN Druckman, ... Advances in experimental political science 19, 19-41, 2021 | 214* | 2021 |
| Beyond the breaking point? Survey satisficing in conjoint experiments K Bansak, J Hainmueller, DJ Hopkins, T Yamamoto Political Science Research and Methods 9 (1), 53-71, 2021 | 161 | 2021 |
| Identifying mechanisms behind policy interventions via causal mediation analysis L Keele, D Tingley, T Yamamoto Journal of Policy Analysis and Management 34 (4), 937-963, 2015 | 151 | 2015 |
| Measuring voters’ multidimensional policy preferences with conjoint analysis: Application to Japan’s 2014 election Y Horiuchi, DM Smith, T Yamamoto Political analysis 26 (2), 190-209, 2018 | 129 | 2018 |
| Causal inference with differential measurement error: Nonparametric identification and sensitivity analysis K Imai, T Yamamoto American Journal of Political Science 54 (2), 543-560, 2010 | 119* | 2010 |
| Identifying voter preferences for politicians’ personal attributes: A conjoint experiment in Japan Y Horiuchi, DM Smith, T Yamamoto Political Science Research and Methods 8 (1), 75-91, 2020 | 116 | 2020 |
| Using conjoint experiments to analyze election outcomes: The essential role of the average marginal component effect K Bansak, J Hainmueller, DJ Hopkins, T Yamamoto Political Analysis 31 (4), 500-518, 2023 | 114 | 2023 |
| Persuading the enemy: Estimating the persuasive effects of partisan media with the preference-incorporating choice and assignment design J de Benedictis-Kessner, MA Baum, AJ Berinsky, T Yamamoto American Political Science Review 113 (4), 902-916, 2019 | 95 | 2019 |
| Comment on Pearl: Practical implications of theoretical results for causal mediation analysis. K Imai, L Keele, D Tingley, T Yamamoto American Psychological Association 19 (4), 482, 2014 | 50 | 2014 |
| Identification and estimation of causal mediation effects with treatment noncompliance T Yamamoto Unpublished manuscript, 2013 | 44 | 2013 |
Kosuke ImaiProfessor of Government and of Statistics, Harvard UniversityVerified email at Harvard.Edu
