![]() ![]() We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. However, failure to capture all confounding comes at a price the static optimality of the resulting rules becomes origin-specific. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. The current article provides an important advance on Petersen et. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. ![]() (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. A statically optimal individualized treatment rule (as introduced in van der Laan et. ![]() The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment RulesĬonsider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. ![]()
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