Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

arXiv:2211.01939v3 Announce Type: replace
Abstract: 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 …

abstract analysis arxiv causal causal inference counterfactual cs.ai cs.lg inference machine machine learning model selection observe stat.me study treatment type validation