Many modern applications require the use of data to both select the statistical tasks and make valid inference after selection. In this article, we provide a unifying approach to control for a class of selective risks. Our method is motivated by a …
In a special workshop in ACIC 2018, we were invited to analyze a simulated dataset to detect treatment effect heterogeneity. This article reports our results presented in the workshop. We also tried out more recent selective inference methods based …
We approach the heterogeneous treatment effect problem in a novel way. Instead of trying to obtain the optimal treatment regime, we seek an interpretable model for effect modification using the recently developed selective inference framework.
Qualitative interaction is an extreme form of treatment effect heterogeneity where the treatment can be beneficial for some but harmful for others. We formulated this question as a global testing problem with many conservative null $p$-values and …