26 research outputs found
Nonparametric Estimation of Conditional Incremental Effects
Conditional effect estimation has great scientific and policy importance
because interventions may impact subjects differently depending on their
characteristics. Most research has focused on estimating the conditional
average treatment effect (CATE). However, identification of the CATE requires
all subjects have a non-zero probability of receiving treatment, or positivity,
which may be unrealistic in practice. Instead, we propose conditional effects
based on incremental propensity score interventions, which are stochastic
interventions where the odds of treatment are multiplied by some factor. These
effects do not require positivity for identification and can be better suited
for modeling scenarios in which people cannot be forced into treatment. We
develop a projection estimator and a flexible nonparametric estimator that can
each estimate all the conditional effects we propose and derive model-agnostic
error guarantees showing both estimators satisfy a form of double robustness.
Further, we propose a summary of treatment effect heterogeneity and a test for
any effect heterogeneity based on the variance of a conditional derivative
effect and derive a nonparametric estimator that also satisfies a form of
double robustness. Finally, we demonstrate our estimators by analyzing the
effect of intensive care unit admission on mortality using a dataset from the
(SPOT)light study