Honesty in Causal Forests: When It Helps and When It Hurts
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| Format: | Preprint |
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2025
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| _version_ | 1866915832565596160 |
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| author | Hou, Yanfang Fernández-Loría, Carlos |
| author_facet | Hou, Yanfang Fernández-Loría, Carlos |
| contents | Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard modeling practice with this method is honest estimation: dividing the data into two samples, one to define subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages. But is it the right choice? In this paper, we show that honest estimation can reduce the accuracy of individual-level treatment effect estimates, especially when there are substantial differences in how individuals respond to treatment, and the data is rich enough to uncover those differences. The core issue is a classic bias-variance trade-off: honesty lowers the risk of overfitting but increases the risk of underfitting, because it limits the data available to detect and model heterogeneity. Across 7,500 benchmark datasets, we find that the cost of using honesty by default can be as high as requiring 25% more data to match the performance of models trained without it. We argue that honesty is best understood as a form of regularization and its use should be guided by application goals and empirical evaluation, not adopted reflexively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Honesty in Causal Forests: When It Helps and When It Hurts Hou, Yanfang Fernández-Loría, Carlos Machine Learning Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard modeling practice with this method is honest estimation: dividing the data into two samples, one to define subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages. But is it the right choice? In this paper, we show that honest estimation can reduce the accuracy of individual-level treatment effect estimates, especially when there are substantial differences in how individuals respond to treatment, and the data is rich enough to uncover those differences. The core issue is a classic bias-variance trade-off: honesty lowers the risk of overfitting but increases the risk of underfitting, because it limits the data available to detect and model heterogeneity. Across 7,500 benchmark datasets, we find that the cost of using honesty by default can be as high as requiring 25% more data to match the performance of models trained without it. We argue that honesty is best understood as a form of regularization and its use should be guided by application goals and empirical evaluation, not adopted reflexively. |
| title | Honesty in Causal Forests: When It Helps and When It Hurts |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.13107 |