1 to 6 of 6 Results
Oct 1, 2014 - Political Analysis Dataverse
Hainmueller, Jens; Hopkins, Daniel J.; Yamamoto, Teppei, 2013, "Replication data for: Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments", https://doi.org/10.7910/DVN/THJYQR, Harvard Dataverse, V2, UNF:5:A5Jv8XldU7R73/D1xTL3Gw== [fileUNF]
Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causa... |
Oct 21, 2012
Imai, Kosuke; Yamamoto, Teppei, 2012, "Replication data for: Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments", https://doi.org/10.7910/DVN/OU6D17, Harvard Dataverse, V1
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are caus... |
Sep 28, 2011
Kosuke Imai; Dustin Tingley; Teppei Yamamoto, 2011, "Replication data for: Experimental Designs for Identifying Causal Mechanisms", https://doi.org/10.7910/DVN/LMC3FM, Harvard Dataverse, V1, UNF:5:knfQrHu8s/s7cnu4nIlUXg== [fileUNF]
Experimentation is a powerful methodology that enables scientists to empirically establish causal claims. However, one important criticism is that experiments merely provide a black-box view of causality and fail to identify causal mechanisms. Specifically, critics argue that although experiments can identify average causal effects, they cannot exp... |
Aug 7, 2011
Teppei Yamamoto, 2011, "Replication data for: Understanding the Past: Statistical Analysis of Causal Attribution", https://doi.org/10.7910/DVN/RDAJDB, Harvard Dataverse, V2
Would the third-wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between non-democracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. Th... |
Mar 14, 2010
Kosuke Imai; Luke Keele; Teppei Yamamoto, 2010, "Replication data for: Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects", https://doi.org/10.7910/DVN/APNMP7, Harvard Dataverse, V1
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal paths between the treatment and outcome variables. In this paper, we first prove that under a partic... |
Jan 25, 2010
Kosuke Imai; Teppei Yamamoto, 2010, "Replication data for: Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis", https://doi.org/10.7910/DVN/TZOGL9, Harvard Dataverse, V1
Political scientists have long been concerned about the validity of survey measurements. Although many have studied classical measurement error in linear regression models where the error is assumed to arise completely at random, in a number of situations the error may be correlated with the outcome. We analyze the impact of differential measuremen... |
