Abstract
In most medical research, the average treatment effect is used to evaluate a treatment’s performance. However, precision medicine requires knowledge of individual treatment effects: What is the difference between a unit’s measurement under treatment and control conditions? In most treatment effect studies, such answers are not possible because the outcomes under both experimental conditions are not jointly observed. This makes the problem of causal inference a missing data problem. We propose to solve this problem by …
| Original language | English |
|---|---|
| Publisher | arXiv |
| Pages | 1-37 |
| DOIs | |
| Publication status | Published - 2022 |
Keywords
- Multiple imputation
- joint modeling imputation
- iterativeimputation
- multivariate data analysis