Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

Understand

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.