An introduction to causal inference for pharmacometricians
Abstract As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference c...
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| Main Authors: | , , |
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| Format: | Book |
| Published: |
Wiley,
2023-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | Abstract As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g‐formula, and directed acyclic graphs (DAGs). |
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| Item Description: | 2163-8306 10.1002/psp4.12894 |