Applied work often studies the effect of a binary variable ("treatment")
using linear models with additive effects. I study the interpretation of the
OLS estimands in such models when treatment effects are heterogeneous. I show
that the treatment coefficient is a convex combination of two parameters, which
under certain conditions can be interpreted as the average treatment effects on
the treated and untreated. The weights on these parameters are inversely
related to the proportion of observations in each group. Reliance on these
implicit weights can have serious consequences for applied work, as I
illustrate with two well-known applications. I develop simple diagnostic tools
that empirical researchers can use to avoid potential biases. Software for
implementing these methods is available in R and Stata. In an important special
case, my diagnostics only require the knowledge of the proportion of treated
units
