aipw: Augmented inverse probability weighted estimator (AIPW)
In idiazst/causal-quantile: Estimate quantile treatment effects (Causal Quantile)
Description
Usage
Arguments
Value
Description
Augmented inverse probability weighted estimator (AIPW)
Usage
1
Arguments
y
Vector with outcome values.
t
Vector with binary treatment indicator.
Q
Conditional outcome distribution estimate. This should come in the for of an n x p matrix, where each column represents a conditional quantile. (See Kang & Schafer example)
g
Propensity score.
q
Quantile to be computed (e.g., q = 0.5 for the median.)
Value
A point estimate
idiazst/causal-quantile documentation built on May 18, 2019, 2:32 a.m.
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Description Usage Arguments Value
Description
Augmented inverse probability weighted estimator (AIPW)
Usage
1 |
Arguments
y |
Vector with outcome values. |
t |
Vector with binary treatment indicator. |
Q |
Conditional outcome distribution estimate. This should come in the for of an n x p matrix, where each column represents a conditional quantile. (See Kang & Schafer example) |
g |
Propensity score. |
q |
Quantile to be computed (e.g., q = 0.5 for the median.) |
Value
A point estimate
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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