A0908
Title: Nonparametric assessment of regimen response curve estimators
Authors: Ashkan Ertefaie - University of Rochester (United States) [presenting]
Cuong Pham - University of Rochester (United States)
Benjamin Baer - University of St Andrews (United Kingdom)
Abstract: 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. A dynamic marginal structural model is introduced that can be used to estimate an optimal decision rule within a class of parametric rules. Specifically, the mean outcome is estimated 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, the risk is leveraged to assess the goodness-of-fit of the imposed working model. The counterfactual risk is considered as the target parameter, and inverse probability weighting and canonical gradients are derived to map it to the observed data. Asymptotic properties of the resulting risk estimators are provided, considering both fixed and data-dependent target parameters. It is shown that the inverse probability weighting estimator can be efficient and asymptotic linear when the weight functions are estimated using a sieve-based estimator.
