论文标题
异质因果效应估计的模型选择的经验分析
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
论文作者
论文摘要
我们研究因果推断中的模型选择问题，特别是有条件的平均治疗效果（CATE）估计。与机器学习不同，对于模型选择，没有完美的交叉验证类似物，因为我们没有观察到反事实的潜在结果。为此，已经提出了仅使用观察到的数据的CATE模型选择的各种替代指标。但是，由于先前研究中的比较有限，我们对它们的有效性没有很好的了解。我们进行了广泛的经验分析，以基于文献中引入的替代模型选择指标以及这项工作中引入的新颖的指标。我们通过通过AUTOML调整与这些指标相关的超参数来确保进行公平的比较，并通过通过生成建模通过合并现实的数据集来提供更详细的趋势。我们的分析提出了基于仔细的CATE估计量和因果结合的仔细的超参数选择的新型模型选择策略。
We study the problem of model selection in causal inference, specifically for conditional average treatment effect (CATE) estimation. Unlike machine learning, there is no perfect analogue of cross-validation for model selection as we do not observe the counterfactual potential outcomes. Towards this, a variety of surrogate metrics have been proposed for CATE model selection that use only observed data. However, we do not have a good understanding regarding their effectiveness due to limited comparisons in prior studies. We conduct an extensive empirical analysis to benchmark the surrogate model selection metrics introduced in the literature, as well as the novel ones introduced in this work. We ensure a fair comparison by tuning the hyperparameters associated with these metrics via AutoML, and provide more detailed trends by incorporating realistic datasets via generative modeling. Our analysis suggests novel model selection strategies based on careful hyperparameter selection of CATE estimators and causal ensembling.
