Visual Heuristics for Marginal Effects Plots
15 Pages Posted: 14 Apr 2017
Date Written: April 13, 2017
Common visual heuristics used to interpret marginal effects plots are susceptible to Type-1 error. This susceptibility varies as a function of (1) sample size, (2) stochastic error in the true data generating process, and (3) the relative size of the main effects of the causal variable versus the moderator. I discuss simple alternatives to these standard visual heuristics that may improve inference and do not depend on regression parameters.
Keywords: interaction terms, marginal effects
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