ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912157988290560 |
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| author | Cha, Taehun Lee, Donghun |
| author_facet | Cha, Taehun Lee, Donghun |
| contents | In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria \citep{cohn1994active}. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments Cha, Taehun Lee, Donghun Machine Learning Artificial Intelligence Methodology In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a policy minimizing an estimation error on conditional average treatment effect is equivalent to minimizing an integrated posterior variance, similar to Cohn criteria \citep{cohn1994active}. We theoretically prove ABC3 also minimizes an imbalance between the treatment and control groups and the type 1 error probability. Imbalance-minimizing characteristic is especially notable as several works have emphasized the importance of achieving balance. Through extensive experiments on real-world data sets, ABC3 achieves the highest efficiency, while empirically showing the theoretical results hold. |
| title | ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2412.11104 |