Do Contemporary CATE Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark

• URL: http://arxiv.org/abs/2410.07021v1
• Date: Wed, 9 Oct 2024 16:04:40 GMT
• Title: Do Contemporary CATE Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
• Authors: Haining Yu, Yizhou Sun,
• Abstract summary: We present unexpected findings from a large-scale benchmark study evaluating Conditional Average Treatment Effect (CATE) estimation algorithms. By running 16 modern CATE models across 43,200 datasets, we find that: (a) 62% of CATE estimates have a higher Mean Squared Error (MSE) than a trivial zero-effect predictor, rendering them ineffective; (b) in datasets with at least one useful CATE estimate, 80% still have higher MSE than a constant-effect model; and (c) Orthogonality-based models outperform other models only 30% of the time, despite widespread
• Score: 39.06952509635041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present unexpected findings from a large-scale benchmark study evaluating Conditional Average Treatment Effect (CATE) estimation algorithms. By running 16 modern CATE models across 43,200 datasets, we find that: (a) 62\% of CATE estimates have a higher Mean Squared Error (MSE) than a trivial zero-effect predictor, rendering them ineffective; (b) in datasets with at least one useful CATE estimate, 80\% still have higher MSE than a constant-effect model; and (c) Orthogonality-based models outperform other models only 30\% of the time, despite widespread optimism about their performance. These findings expose significant limitations in current CATE models and suggest ample opportunities for further research. Our findings stem from a novel application of \textit{observational sampling}, originally developed to evaluate Average Treatment Effect (ATE) estimates from observational methods with experiment data. To adapt observational sampling for CATE evaluation, we introduce a statistical parameter, $Q$, equal to MSE minus a constant and preserves the ranking of models by their MSE. We then derive a family of sample statistics, collectively called $\hat{Q}$, that can be computed from real-world data. We prove that $\hat{Q}$ is a consistent estimator of $Q$ under mild technical conditions. When used in observational sampling, $\hat{Q}$ is unbiased and asymptotically selects the model with the smallest MSE. To ensure the benchmark reflects real-world heterogeneity, we handpick datasets where outcomes come from field rather than simulation. By combining the new observational sampling method, new statistics, and real-world datasets, the benchmark provides a unique perspective on CATE estimator performance and uncover gaps in capturing real-world heterogeneity.

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