Estimating the Long-Term Effects of Novel Treatments

被引:0
作者
Battocchi, Keith [1 ]
Dillon, EleanorW. [1 ]
Hei, Maggie [1 ]
Lewis, Greg [1 ]
Oprescu, Miruna [1 ]
Syrgkanis, Vasilis [1 ]
机构
[1] Microsoft Res, Auburndale, MS USA
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) | 2021年 / 34卷
关键词
CAUSAL INFERENCE; END-POINTS; MODELS; TRIALS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Policy makers often need to estimate the long-term effects of newly-developed treatments, while only having historical data of older treatment options. We propose a surrogate-based approach using a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. Our approach generalizes previous surrogate-style methods, allowing for continuous treatments and serially-correlated treatment policies while maintaining consistency and root-n asymptotically normal estimates under a Markovian assumption on the data and the observational policy. Using a semi-synthetic dataset on customer incentives from a major corporation, we evaluate the performance of our method and discuss solutions to practical challenges when deploying our methodology.
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页数:11
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