Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929494738075648 |
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| author | Souto, Hugo Gobato Neto, Francisco Louzada |
| author_facet | Souto, Hugo Gobato Neto, Francisco Louzada |
| contents | This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables. Across three distinct sets of Data Generating Processes (DGPs), the ps-BART model consistently outperforms the BCF model, particularly in highly nonlinear settings. The ps-BART model's robustness in uncertainty estimation and accuracy in both point-wise and probabilistic estimation demonstrate its utility for real-world applications. This research fills a crucial gap in causal inference literature, providing a tool better suited for nonlinear treatment-outcome relationships and opening avenues for further exploration in the domain of continuous treatment effect estimation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06593 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments Souto, Hugo Gobato Neto, Francisco Louzada Machine Learning Applications This paper introduces a generalized ps-BART model for the estimation of Average Treatment Effect (ATE) and Conditional Average Treatment Effect (CATE) in continuous treatments, addressing limitations of the Bayesian Causal Forest (BCF) model. The ps-BART model's nonparametric nature allows for flexibility in capturing nonlinear relationships between treatment and outcome variables. Across three distinct sets of Data Generating Processes (DGPs), the ps-BART model consistently outperforms the BCF model, particularly in highly nonlinear settings. The ps-BART model's robustness in uncertainty estimation and accuracy in both point-wise and probabilistic estimation demonstrate its utility for real-world applications. This research fills a crucial gap in causal inference literature, providing a tool better suited for nonlinear treatment-outcome relationships and opening avenues for further exploration in the domain of continuous treatment effect estimation. |
| title | Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2409.06593 |