# Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models

arXiv: https://arxiv.org/abs/1909.05299 · HTML: https://mlsanity.com/paper/1909.05299/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Causal Effect Variational Autoencoder with Uniform Treatment](https://mlsanity.com/paper/2111.08656/) (2021)
- [Ensemble Method for Estimating Individualized Treatment Effects](https://mlsanity.com/paper/2202.12445/) (2022)
- [Active and Passive Causal Inference Learning](https://mlsanity.com/paper/2308.09248/) (2023)

## Abstract (arXiv)

We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the performance of candidate CATE predictors to enable accurate and stable model selection. To this end, we analyze the model performance ranking problem and formulate guidelines to obtain a better evaluation metric. We then propose a novel metric that can identify the ranking of the performance of CATE predictors with high confidence. Empirical evaluations demonstrate that our metric outperforms existing metrics in both model selection and hyperparameter tuning tasks.