41 to 50 of 80 Results
Oct 20, 2021 - Harvard Dataverse
Olivella, Santiago; Pratt, Tyler; Imai, Kosuke, 2019, "Replication Data for: Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts", https://doi.org/10.7910/DVN/82CULX, Harvard Dataverse, V6
Data and code required for reproducing all results reported in "Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts". |
Imai, Kosuke; Kim, In Song; Wang, Erik, 2021, "Replication Data for: Matching Methods for Causal Inference with Time-Series Cross-Section Data", https://doi.org/10.7910/DVN/ZTDHVE, Harvard Dataverse, V1, UNF:6:2b7e6QWN2z+/aUZBMHX3hQ== [fileUNF]
Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first ma... |
Apr 24, 2021 - Harvard Dataverse
Kosuke Imai; Michael Lingzhi Li, 2021, "Replication Data for: Experimental Evaluation of Individualized Treatment Rules", https://doi.org/10.7910/DVN/YCYTOB, Harvard Dataverse, V1, UNF:6:tJ0WQDm2Q04jLwTUHtwIcA== [fileUNF]
This repository contains the data and code necessary to replicate the results in the paper "Experimental Evaluation of Individualized Treatment Rules" for both the synthetic and the real-world experiments. |
Rosenfeld, Bryn; Imai, Kosuke; Shapiro, Jacob, 2015, "Replication Data for: An Empirical Validation Study of Popular Survey Methodologies for Sensitive Questions", https://doi.org/10.7910/DVN/29911, Harvard Dataverse, V3, UNF:5:wfSfR7xnbL9XigVosud4zA== [fileUNF]
When studying sensitive issues including corruption, prejudice, and sexual behavior, researchers have increasingly relied upon indirect questioning techniques to mitigate such known problems of direct survey questions as under-reporting and nonresponse. However, there have been surprisingly few empirical validation studies of these indirect techniq... |
Imai, Kosuke; Lo, James, 2020, "Replication Data for: Robustness of Empirical Evidence for the Democratic Peace", https://doi.org/10.7910/DVN/QEGXSZ, Harvard Dataverse, V3
Replication archive for: Robustness of Empirical Evidence for the Democratic Peace: A Nonparametric Sensitivity Analysis, conditionally accepted in International Organization |
de la Cuesta, Brandon; Egami, Naoki; Imai, Kosuke, 2020, "Replication Data for: Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution", https://doi.org/10.7910/DVN/HVY5GR, Harvard Dataverse, V1
Conjoint analysis has become popular among social scientists for measuring multidimensional preferences. When analyzing such experiments, researchers often focus on the average marginal component effect (AMCE), which represents the causal effect of a single profile attribute while averaging over the remaining attributes. What has been overlooked, h... |
Fifield, Benjamin; Imai, Kosuke; Kawahara, Jun; Kenny, Christopher T, 2020, "Replication Data for: The Essential Role of Empirical Validation in Legislative Redistricting Simulation", https://doi.org/10.7910/DVN/NH4CRS, Harvard Dataverse, V1
As granular data about elections and voters become available, redistricting simulation methods are playing an increasingly important role when legislatures adopt redistricting plans and courts determine their legality. These simulation methods are designed to yield a representative sample of all redistricting plans that satisfy statutory guidelines... |
Jun 16, 2020
Imai, Kosuke; Jiang, Zhichao; Malani, Anup, 2020, "Replication Data for: Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments.", https://doi.org/10.7910/DVN/N7D9LS, Harvard Dataverse, V1
In many social science experiments, subjects often interact with each other and as a result one unit's treatment influences the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of tr... |
Mar 22, 2020 - Harvard Dataverse
Zhao, Shandong; van Dyk, David; Imai, Kosuke, 2020, "Replication Data for: "Propensity-Score Based Methods for Causal Inference in Observational Studies with Non-Binary Treatments."", https://doi.org/10.7910/DVN/LW2JVY, Harvard Dataverse, V1
Propensity score methods are a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. Such extensions have wid... |
Fifield, Benjamin; Michael Higgins; Kosuke Imai; Alexander Tarr, 2020, "Replication Data for: Automated Redistricting Simulation Using Markov Chain Monte Carlo", https://doi.org/10.7910/DVN/VCIW2I, Harvard Dataverse, V1
Legislative redistricting is a critical element of representative democracy. A number of political scientists have used simulation methods to sample redistricting plans under various constraints in order to assess their impact on partisanship and other aspects of representation. However, while many optimization algorithms have been proposed, surpri... |
