The reasons people start treatment are not the same as the reasons they stop

And what this implies for causal inference for sustained treatment strategies.

causal inference
code
g-formula
Author
Affiliation

Christopher Boyer

Harvard TH Chan School of Public Health

Published

February 16, 2024

Researchers are often interested in estimating the effects of sustained use of a treatment on a health outcome.

\[\begin{align} L_0 \sim A_0 \sim L_1 \sim A_1 \sim Y \end{align}\]

A directed acyclic graph for a single, time-fixed treatment.

Reuse

Citation

BibTeX citation:
@online{boyer2024,
  author = {Boyer, Christopher},
  title = {The Reasons People Start Treatment Are Not the Same as the
    Reasons They Stop},
  date = {2024-02-16},
  langid = {en}
}
For attribution, please cite this work as:
Boyer, Christopher. 2024. “The Reasons People Start Treatment Are Not the Same as the Reasons They Stop.” February 16, 2024.