Mycroft Holmes, Sherlock's elder brother, was a government official who
specialized in taking together facts from a variety of disparate sources and
putting together the best course of government action. His deductive skills
and logical reasoning abilities exceeded even those of Sherlock. Mycroft was
concerned more with the "big picture" than with the details of particular
cases. As such, the role of mycroft_assess within the sherlock
causal segmentation framework is to evaluate the population-level causal
effects that would result from following the course of action (i.e., dynamic
treatment) prescribed by Sherlock and Watson. For mycroft_assess to
work, both sherlock_calculate and watson_segment
must be called in sequence to evaluate the segmentation case and assign a
treatment decision to segments.
mycroft_assess(data_from_advising, param_type = c("hte", "ote"),
est_type = c("onestep"))
Arguments
| data_from_advising |
A data.table containing
the output from successive calls to sherlock_calculate and
watson_segment, which matches the input data, augmented with
cross-validated nuisance parameter estimates and an estimate of the CATE. A
summary of the estimates across segmentation strata is made available as an
attribute of the data.table. |
| param_type |
A character providing a specification for a family
of target parameters to be estimated. The two choices provide estimates of
a range of target parameters. Specifically, the choices correspond to
"hte", which computes the average treatment effect (ATE), which
contrasts static interventions for treatment assignment and treatment
being withhold, for (1) the full population, (2) within the subgroup of
units dynamically assigned treatment, and (3) within the subgroup of
units from whom treatment was withheld dynamically. Also included is the
heterogeneous treatment effect (HTE), defined as a difference of the two
subgroup ATEs, which captures the benefit attributable to treating those
units who should receive treatment and withholding treatment from those
that could be harmed by the treatment.
"ote", which computes several counterfactual means, for (1) the
static intervention of assigning treatment to all units, (2) the static
intervention of withholding treatment from all units, (3) the dynamic
intervention of assigning treatment to those predicted to benefit from
it while withholding treatment from those units that could be harmed. In
addition, two average treatment effects are evaluated, each contrasting
the dynamic treatment rule against the static interventions of assigning
or withholding treatment. The latter three parameters capture contrasts
based on the optimal treatment effect (OTE).
|
| est_type |
A character specifying the type of estimator to be
computed. Both estimators are asymptotically linear when flexible modeling
choices are used to estimate nuisance parameters, doubly robust (consistent
when at least one nuisance parameter is correctly estimated), and achieve
the best possible variance (i.e., asymptotically efficient) among the class
of regular asymptotically linear estimators. The two options are
"onestep", corresponding to the one-step estimator, a first-order
solution to the efficient influence function (EIF) estimating equation.
This is not a substitution (direct) estimator and may be unstable in the
sense of yielding estimates outside the bounds of the target parameter.
"tmle", corresponding to the targeted minimum loss estimator, an
approach that updates initial estimates of the outcome model by way of a
one-dimensional fluctuation model that aims to approximately solve the
EIF estimating equation.
|