Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
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2020
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| author | Bargagli-Stoffi, Falco J. Cadei, Riccardo Lee, Kwonsang Dominici, Francesca |
| author_facet | Bargagli-Stoffi, Falco J. Cadei, Riccardo Lee, Kwonsang Dominici, Francesca |
| contents | In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average. Decision trees have been proposed and commonly adopted for the data-driven discovery of HTE due to their high level of interpretability. However, single-tree discovery of HTE can be unstable and oversimplified. This paper introduces the Causal Rule Ensemble (CRE), a new method for HTE discovery and estimation using an ensemble-of-trees approach. CRE offers several key features, including 1) an interpretable representation of the HTE; 2) the ability to explore complex heterogeneity patterns; and 3) high stability in subgroups discovery. The discovered subgroups are defined in terms of interpretable decision rules. Estimation of subgroup-specific causal effects is performed via a two-stage approach, for which we provide theoretical guarantees. Through simulations, we show that the CRE method is highly competitive compared to state-of-the-art techniques. Finally, we apply CRE to discover the heterogeneous health effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_09036 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects Bargagli-Stoffi, Falco J. Cadei, Riccardo Lee, Kwonsang Dominici, Francesca Methodology Machine Learning In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average. Decision trees have been proposed and commonly adopted for the data-driven discovery of HTE due to their high level of interpretability. However, single-tree discovery of HTE can be unstable and oversimplified. This paper introduces the Causal Rule Ensemble (CRE), a new method for HTE discovery and estimation using an ensemble-of-trees approach. CRE offers several key features, including 1) an interpretable representation of the HTE; 2) the ability to explore complex heterogeneity patterns; and 3) high stability in subgroups discovery. The discovered subgroups are defined in terms of interpretable decision rules. Estimation of subgroup-specific causal effects is performed via a two-stage approach, for which we provide theoretical guarantees. Through simulations, we show that the CRE method is highly competitive compared to state-of-the-art techniques. Finally, we apply CRE to discover the heterogeneous health effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S. |
| title | Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2009.09036 |