@kosuke_imai
⚠️ Unofficial community showcase of Prof. Kosuke Imai's packages (mediation, panel matching, measurement). Not affiliated — all credit to the original authors; this summarizes public docs.
Workflows
Sensitive questions, protected answers (rr)
Regression for randomized-response surveys — recover predictors of a sensitive behavior while every respondent's individual answer stays private.
Who responds to treatment? (FindIt)
Find which subgroups respond to a treatment and estimate causal interactions in factorial / conjoint experiments via a LASSO-regularized search.
Design & analyze randomized experiments (experiment)
Randomize treatment (complete, blocked, cluster) and estimate average effects with design-based variance — including cluster-randomized trials.
Quantitative Social Science: data and code (qss)
The companion R package for Imai's textbook Quantitative Social Science, bundling every dataset and chapter vignette for hands-on data-analysis teaching.
Analyzing list / item-count experiments (list)
Multivariate regression for list (item-count) experiments, recovering the prevalence and predictors of a sensitive attitude without asking about it directly.
Predicting race/ethnicity from name and geography (wru)
"Who Are You?" predicts an individual's probable race/ethnicity from surname, first/middle name, and geolocation using Bayesian (BISG) updating.
Matching for panel / time-series cross-sectional data (PanelMatch)
Matches treated unit-periods to controls with identical recent treatment histories, then applies a difference-in-differences estimator for TSCS data.
Causal mediation analysis (mediation)
Decomposes a treatment effect into the part transmitted through a mediator (ACME) and the rest (direct effect), with sensitivity analysis.