  Laparoscopic surgery has been shown through a number of randomized trials to
be an effective form of treatment for cholecystitis. Given this evidence, one
natural question for clinical practice is: does the effectiveness of
laparoscopic surgery vary among patients? It might be the case that, while the
overall effect is positive, some patients treated with laparoscopic surgery may
respond positively to the intervention while others do not or may be harmed. In
our study, we focus on conditional average treatment effects to understand
whether treatment effects vary systematically with patient characteristics.
Recent methodological work has developed a meta-learner framework for flexible
estimation of conditional causal effects. In this framework, nonparametric
estimation methods can be used to avoid bias from model misspecification while
preserving statistical efficiency. In addition, researchers can flexibly and
effectively explore whether treatment effects vary with a large number of
possible effect modifiers. However, these methods have certain limitations. For
example, conducting inference can be challenging if black-box models are used.
Further, interpreting and visualizing the effect estimates can be difficult
when there are multi-valued effect modifiers. In this paper, we develop new
methods that allow for interpretable results and inference from the
meta-learner framework for heterogeneous treatment effects estimation. We also
demonstrate methods that allow for an exploratory analysis to identify possible
effect modifiers. We apply our methods to a large database for the use of
laparoscopic surgery in treating cholecystitis. We also conduct a series of
simulation studies to understand the relative performance of the methods we
develop. Our study provides key guidelines for the interpretation of
conditional causal effects from the meta-learner framework.
