Teppei Yamamoto
Statistics and Probability · 2010–2018
A researcher in the Science corpus: 8 papers; trusts 39 researchers and is trusted by 89 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 74 citations into it; #629 of 12,932 by trust here, #422 by citations
- Causal Inference & Discovery — 34 citations into it; #65 of 364 by trust here, #69 by citations
- Economics and Econometrics — 29 citations into it; #4,922 of 23,481 by trust here, #8,289 by citations
- Sociology and Political Science — 25 citations into it; #642 of 22,756 by trust here, #865 by citations
- Management Science and Operations Research — 14 citations into it
- Artificial Intelligence — 10 citations into it; #4,398 of 35,345 by trust here, #7,010 by citations
- Political Science and International Relations — 10 citations into it; #886 of 4,701 by trust here, #1,145 by citations
- Experimental and Cognitive Psychology — 9 citations into it
Whom Teppei Yamamoto trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- Kosuke Imai
- James M. Robins
- David P. MacKinnon
- Judea Pearl
- Dustin Tingley
- Luke Keele
- Guido W. Imbens
- Rune Slothuus
- Paul Holland
Who trusts Teppei Yamamoto overall
- Judea Pearl
- Kosuke Imai
- Martin Huber
- Dustin Tingley
- Kristopher J Preacher
- Tyler J. VanderWeele
- Luke Keele
- Alessandra Mattei
- Rik Pieters
- Stijn Vansteelandt
Papers from Teppei Yamamoto's perspective
Ordered by how strongly the researchers Teppei Yamamoto trusts endorse each paper. Same corpus, a different order for every researcher.
- Statistics and Causal Inference
- [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies
- Identifiability and Exchangeability for Direct and Indirect Effects
- Bounds on Treatment Effects from Studies with Imperfect Compliance
- Direct and Indirect Effects
- Marginal Structural Models versus Structural nested Models as Tools for Causal inference
- Design of Experiments
- The central role of the propensity score in observational studies for causal effects
- Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
- Experimental and Quasi-Experimental Designs for Generalized Causal Inference
Their papers
- Identification, Inference and Sensitivity Analysis for Causal Mediation Effects (2010) — 1,746 citations
- Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments (2013) — 396 citations
- Experimental designs for identifying causal mechanisms (2013) — 410 citations
- Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies (2011) — 1,378 citations
- mediation: R Package for Causal Mediation Analysis (2014) — 4,010 citations
- Validating vignette and conjoint survey experiments against real-world behavior (2015) — 1,152 citations
- Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments (2013) — 1,787 citations
- Identifying voter preferences for politicians’ personal attributes: a conjoint experiment in Japan (2018) — 89 citations