  Marginal structural models have been widely used in causal inference to
estimate mean outcomes under either a static or a prespecified set of treatment
decision rules. This approach requires imposing a working model for the mean
outcome given a sequence of treatments and possibly baseline covariates. In
this paper, we introduce a dynamic marginal structural model that can be used
to estimate an optimal decision rule within a class of parametric rules.
Specifically, we will estimate the mean outcome as a function of the parameters
in the class of decision rules, referred to as a regimen-response curve. In
general, misspecification of the working model may lead to a biased estimate
with questionable causal interpretability. To mitigate this issue, we will
leverage risk to assess "goodness-of-fit" of the imposed working model. We
consider the counterfactual risk as our target parameter and derive inverse
probability weighting and canonical gradients to map it to the observed data.
We provide asymptotic properties of the resulting risk estimators, considering
both fixed and data-dependent target parameters. We will show that the inverse
probability weighting estimator can be efficient and asymptotic linear when the
weight functions are estimated using a sieve-based estimator. The proposed
method is implemented on the LS1 study to estimate a regimen-response curve for
patients with Parkinson's disease.
