As more and more fields like health economics and outcomes research (HEOR) embrace the enormous potential of data science and become increasingly reliant on modern scientific computing tools, there is a deep need to still understand the foundation on which the capabilities of these modern computing tools rest, what “big data” can and cannot deliver and why, and how to realize a potential of machine learning methods for causal inference. The discussion will focus on areas including:
- The main characteristics of data science and its pertinence to the field of HEOR
- The capabilities of modern computing tools with regard to big data and statistical methods
- What “big data” can and cannot deliver and why, informed by the data science insights
- How to realize a potential of machine learning methods for causal inference
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