  This paper studies settings where the analyst is interested in identifying
and estimating the average causal effect of a binary treatment on an outcome.
We consider a setup in which the outcome realization does not get immediately
realized after the treatment assignment, a feature that is ubiquitous in
empirical settings. The period between the treatment and the realization of the
outcome allows other observed actions to occur and affect the outcome. In this
context, we study several regression-based estimands routinely used in
empirical work to capture the average treatment effect and shed light on
interpreting them in terms of ceteris paribus effects, indirect causal effects,
and selection terms. We obtain three main and related takeaways. First, the
three most popular estimands do not generally satisfy what we call \emph{strong
sign preservation}, in the sense that these estimands may be negative even when
the treatment positively affects the outcome conditional on any possible
combination of other actions. Second, the most popular regression that includes
the other actions as controls satisfies strong sign preservation \emph{if and
only if} these actions are mutually exclusive binary variables. Finally, we
show that a linear regression that fully stratifies the other actions leads to
estimands that satisfy strong sign preservation.
