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Laparoscopic surgery has been shown through a number of randomized trials tobe an effective form of treatment for cholecystitis. Given this evidence, onenatural question for clinical practice is: does the effectiveness oflaparoscopic surgery vary among patients? It might be the case that, while theoverall effect is positive, some patients treated with laparoscopic surgery mayrespond positively to the intervention while others do not or may be harmed. Inour study, we focus on conditional average treatment effects to understandwhether treatment effects vary systematically with patient characteristics.Recent methodological work has developed a meta-learner framework for flexibleestimation of conditional causal effects. In this framework, nonparametricestimation methods can be used to avoid bias from model misspecification whilepreserving statistical efficiency. In addition, researchers can flexibly andeffectively explore whether treatment effects vary with a large number ofpossible effect modifiers. However, these methods have certain limitations. Forexample, conducting inference can be challenging if black-box models are used.Further, interpreting and visualizing the effect estimates can be difficultwhen there are multi-valued effect modifiers. In this paper, we develop newmethods that allow for interpretable results and inference from themeta-learner framework for heterogeneous treatment effects estimation. We alsodemonstrate methods that allow for an exploratory analysis to identify possibleeffect modifiers. We apply our methods to a large database for the use oflaparoscopic surgery in treating cholecystitis. We also conduct a series ofsimulation studies to understand the relative performance of the methods wedevelop. Our study provides key guidelines for the interpretation ofconditional causal effects from the meta-learner framework.

Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients