  Causal decision making (CDM) based on machine learning has become a routine
part of business. Businesses algorithmically target offers, incentives, and
recommendations to affect consumer behavior. Recently, we have seen an
acceleration of research related to CDM and 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 or 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 prior research to highlight three
implications. (1) We should consider carefully the objective function of the
causal machine learning, and if possible, 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 is that for
supporting CDM it may be just as good or even better to learn with confounded
data as with unconfounded data. Finally, (3) causal statistical modeling may
not be necessary to support CDM because a proxy target for statistical modeling
might do as well or better. This third 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. The last two implications
are particularly important in practice, as acquiring (unconfounded) data on all
counterfactuals can be costly and often impracticable. These observations open
substantial research ground. We hope to facilitate research in this area by
pointing to related articles from multiple contributing fields, including two
dozen articles published the last three to four years.
