Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (Springer Series in Statistics)
| Author | : | |
| Rating | : | 4.48 (630 Votes) |
| Asin | : | 3319653032 |
| Format Type | : | paperback |
| Number of Pages | : | 226 Pages |
| Publish Date | : | 2015-10-18 |
| Language | : | English |
DESCRIPTION:
Standard methods and software tools are not currently equipped for these challenges; however, targeted learning is tailored for these problems found in precision medicine, big data, and data science. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data involving time-dependent confounding and censoring as well as other estimands in dependent data structures, such as networks. It presents a scientific roadmap to translate real world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators to construct targeted machine learning algorithms that incorporate the state-of-the-art in machine learning to estimate qu
The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data involving time-dependent confounding and censoring as well as other estimands in dependent data structures, such as networks. Standard methods and software tools are not currently equipped for these challenges; however, targeted learning is tailored for these problems found in precision medicine, big data, and data science. It presents a scientific roadmap to translate real world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators to construct targeted machine learning algorithms that incorporate the state-of-the-art in machine learning to estimate quantities of interest, while still providing valid inference. Targeted learning methods within data science are a critical component for answering&