Last data update: 2014.03.03

R: Calculates propensity score weights
 ps.wgt.fun R Documentation

## Calculates propensity score weights

### Description

Calculates propensity score (or inverse probability of treatment) weights given the treatment indicator and available baseline (pretreatment) covariates.

### Usage

```ps.wgt.fun(treat, cov.for.ps, weight = NULL)
```

### Arguments

 `treat` treatment indicator, should be 0/1. `cov.for.ps` matrix of covariates to be used to estimate propensity score (or inverse probability of treatment) weights `weight` a (n1+n0) by x matrix of weights where n1 = number of observations in treatment group 1 and n0 = number of observations in treatment group 0; used for perturbation-resampling, default is null.

### Details

Let Z_{i} denote the matrix of baseline (pretreatment) covariates and G_i be the treatment group indicator such that G_i = 1 indicates treatment and G_i = 0 indicates control. This function estimates P = P(G_i = 1 | Z_i) using logistic regression. The propensity score (or inverse probability of treatment) weights are then equal to 1/hat{P} for those in treatment group 1 and 1/(1-hat{P}) for those in treatment group 0. These weights reflect the situation where the average treatment effect (ATE) is of interest, not average treatment effect in the treated (ATT).

### Value

propensity score (or inverse probability of treatment) weights

Layla Parast

### References

Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.

Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516-524.

### Examples

```data(example_obs)
W.weight = ps.wgt.fun(treat = example_obs\$treat, cov.for.ps = as.matrix(example_obs\$Z))
delta.iptw.km(tl=example_obs\$TL, dl = example_obs\$DL, treat = example_obs\$treat, tt=2,
ps.weights = W.weight)
```

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