ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments

Autor:	Cha, Taehun, Lee, Donghun
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	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. Comment: AAAI 2025
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.11104 Zobrazit plný text záznamu View this record from Arxiv