# Release history of causal.decomp

#### Suyeon Kang, Soojin Park

The following changes have been made since the initial release of
`causal.decomp`

0.0.1.

## Changes in `causal.decomp`

0.1.0.

- The function
`sensitivity`

is added, which implements the
sensitivity analysis for the causal decomposition analysis. As of
version 0.1.0, the argument `boot.res`

of
`sensitivity`

must be an object generated by `smi`

with a single mediator. The object generated by `sensitivity`

can be visualized in contour plots with robustness values using the
`plot()`

method.
- If non-NULL weights are used in fitting
`fit.m`

and
`fit.y`

, the weights are incorporated in the estimation by
the `smi`

, `mmi`

, or `pocr`

function.
- New data
`sMIDUS`

is added, which is synthetic data
containing variables from actual Midlife Development in the U.S. (MIDUS)
data used in Park et al. (2023). As the actual data is not publicly
available due to confidentiality concerns, `sMIDUS`

is not
directly derived from the actual data but artificially generated to
mimic the actual MIDUS data.

## References

- Park, S., Qin, X., & Lee, C. (2020). Estimation and sensitivity
analysis for causal decomposition in health disparity research.
Sociological Methods & Research, 00491241211067516.
- Park, S., Kang, S., & Lee, C. (2021+). Choosing an optimal
method for causal decomposition analysis: A better practice for
identifying contributing factors to health disparities. arXiv preprint
arXiv:2109.06940.
- Park, S., Kang, S., Lee, C., & Ma, S. (2023). Sensitivity
analysis for causal decomposition analysis: Assessing robustness toward
omitted variable bias,
*Journal of Causal Inference*.
Forthcoming.