Аннотация
What is the most effective way to select the best causal model among
potential candidates? In this paper, we propose a method to effectively select
the best individual-level treatment effect (ITE) predictors from a set of
candidates using only an observational validation set. In model selection or
hyperparameter tuning, we are interested in choosing the best model or the
value of hyperparameter from potential candidates. Thus, we focus on accurately
preserving the rank order of the ITE prediction performance of candidate causal
models. The proposed evaluation metric is theoretically proved to preserve the
true ranking of the model performance in expectation and to minimize the upper
bound of the finite sample uncertainty in model selection. Consistent with the
theoretical result, empirical experiments demonstrate that our proposed method
is more likely to select the best model and set of hyperparameter in both model
selection and hyperparameter tuning.
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