  Causal inference methods have been applied in various fields where
researchers want to estimate treatment effects. In traditional causal inference
settings, one assumes that the outcome of a unit does not depend on treatments
of other units. However, as causal inference methods are extended to more
applications, there is a greater need for estimators of general causal effects.
We use an exposure mapping framework [Aronow and Samii, 2017] to map the
relationship between the treatment allocation and the potential outcomes. Under
the exposure model, we propose linear unbiased estimators (LUEs) for general
causal effects under the assumption that treatment effects are additive.
Additivity provides statistical advantages, where contrasts in exposures are
now equivalent, and so the set of estimators considered grows. We identify a
subset of LUEs that forms an affine basis for LUEs, and we characterize optimal
LUEs with minimum integrated variance through defining conditions on the
support of the estimator. We show, through simulations that our proposed
estimators are fairly robust to violations of the additivity assumption, and in
general, there is benefit in leveraging information from all exposures.
