  One of the main tasks of causal inference is estimating well-defined causal
parameters. One of the main causal parameters is the average causal effect
(ACE) - the expected value of the individual level causal effects in the target
population. For binary treatments, the individual level causal effect is
defined as contrast between potential outcomes. For continuous outcomes,
however, there are many such contrasts in finite samples, thus hampering their
use as a useful summary of the causal relationship. Here, we proposed a
generalized version of the ACE, where individual level causal effects are
defined as the derivative (with respect to the treatment) of the individual
level causal dose-response function evaluated at treatment value that the
individual has. This definition is equivalent to the conventional definition
for binary treatments, but also incorporates continuous treatments. We
demonstrate that this quantity can be estimated under conventional causal
assumptions and illustrate the theoretical ideas with a simulation study.
