  Causal decision making (CDM) at scale has become a routine part of business,
and increasingly CDM is based on machine learning algorithms. For example,
businesses often target offers, incentives, and recommendations with the goal
of affecting consumer behavior. Recently, we have seen an acceleration of
research related to CDM and to causal effect estimation (CEE) using machine
learned models. This article highlights an important perspective: CDM is not
the same as CEE, and counterintuitively, accurate CEE is not necessary for
accurate CDM. Our experience is that this is not well understood by
practitioners nor by most researchers. Technically, the estimand of interest is
different, and this has important implications both for modeling and for the
use of statistical models for CDM. We draw on recent research to highlight
three of these implications. (1) We should carefully consider the objective
function of the causal machine learning, and if possible, we should optimize
for accurate "treatment assignment" rather than for accurate effect-size
estimation. (2) Confounding does not have the same effect on CDM as it does on
CEE. The upshot here is that for supporting CDM it may be just as good to learn
with confounded data as with unconfounded data. Finally, (3) causal statistical
modeling may not be necessary at all to support CDM, because there may be (and
perhaps often is) a proxy target for statistical modeling that can do as well
or better. This observation helps to explain at least one broad common CDM
practice that seems "wrong" at first blush: the widespread use of non-causal
models for targeting interventions. Our perspective is that these observations
open up substantial fertile ground for future research. Whether or not you
share our perspective completely, we hope we facilitate future research in this
area by pointing to related articles from multiple contributing fields.
