# Table of Contents

# Interaction #

- A Tutorial on Interaction by Tyler J. VanderWeele and Mirjam J. Knol
- Interactions by Matt Golder
- How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice
- Understanding Interaction Models: Improving Empirical Analysis

# Marginal effect #

Marginal effect refers to "the slope of the regression surface with respect to a given covariate" (Leeper, 2017). In other words, it is about how much the dependent variable changes when we change the given independent variable. Of course this is not necessarily a constant. The slope can change based on the value of the independent variables. Thus we consider a *representative* marginal effect. A common method is calculating the "average marginal effects (AMEs)", which is the mean of the marginal effect for every data point. Alternatively, the marginal effect can be calculated at the means of independent variables, "marginal effects at means (MEMs)", or at a representative values (MERs). AMEs are most 'data-driven' and reflect the full data distribution.

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