Producing Flexible Marginal Effect Estimates with Multiplicative Interaction Models
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu (Maintainer); Ziyi Liu
This package performs diagnostics and visualizations of multiplicative interaction models. Besides conventional linear interaction models, it provides two additional estimation strategies--linear regression based on pre-specified bins and locally linear regressions based on Gaussian kernels--to flexibly estimate the conditional marginal effect of a treatment variable on an outcome variable across different values of a moderating variable. These approaches relax the linear interaction effect assumption and safeguard against excessive extrapolation.
Jens Hainmueller; Jonathan Mummolo; Yiqing Xu. 2019. "How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice." Political Analysis, Vol. 27, Iss. 2, April 2019, pp. 163--192. Available at: https://www.cambridge.org/core/journals/political-analysis/article/how-much-should-we-trust-estimates-from-multiplicative-interaction-models-simple-tools-to-improve-empirical-practice/D8CAACB473F9B1EE256F43B38E458706.
interflex, plot.interflex, and predict.interflex