Description Format Source References

This simulated dataset is to illustrate how to use sace to estimate the SACE, and compare it with other naive methods. In this simulated data, by design, there is confounding between `Z`

and `Y`

caused by `X`

, and confounding between `S`

and `Y`

caused by `X`

.

A data frame with 5000 observations and 7 variables. `Z`

, `A`

, `Y`

, `S`

are 1-dimensional, and `X`

is 3-dimensional. The variables are as follows:

- Z
Binary treatment

- X.X1
A factor covariate with 2 levels (1 and -1)

- X.V2
A continuous covariate

- X.V3
A contunuous covariate

- A
The substitution variable which is continuous

- Y
The continuous outcome.

`NA`

where*S = 0*- S
The survival indicator.

`1`

means survival and`0`

means death.

The dataset is generated by the simulation design of Wang et al. 2017 with *δ_1 = 1* and *δ_0 = 1*, which allows confounding between `Z`

and `Y`

caused by `X`

, and confounding between `S`

and `Y`

caused by `X`

.

Linbo Wang, Xiao-Hua Zhou, Thomas S. Richardson; Identification and estimation of causal effects with outcomes truncated by death, Biometrika, Volume 104, Issue 3, 1 September 2017, Pages 597-612, https://doi.org/10.1093/biomet/asx034

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