Description Usage Arguments Details Value References Examples

This function implements the Horvitz-Thompson estimator of the mean outcome in the presence of missing data.

1 |

`y` |
A vector or a matrix of outcomes with missing data. |

`tr` |
A vector of non-missing indicators (=1 if |

`p` |
A vector of known or fitted propensity scores. |

`X` |
The model matrix for the propensity score model, assumed to be logistic (set |

`bal` |
Logical; if |

Variance estimation is based on asymptotic expansions, allowing for misspecification of the propensity score model.

For balance checking with `bal=TRUE`

, the input `y`

should correpond to the covariates for which balance is to be checked, and the output `mu`

gives the differences between the Horvitz-Thompson estimates and the overall sample means for these covariates.

`mu` |
The estimated mean(s) or, if |

`v` |
The estimated variance(s) of |

Tan, Z. (2006) "A distributional approach for causal inference using propensity scores," *Journal of the American Statistical Association*, 101, 1619-1637.

Tan, Z. (2010) "Bounded, efficient and doubly robust estimation with inverse weighting,"
*Biometrika*, 97, 661-682.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
data(KS.data)
attach(KS.data)
z=cbind(z1,z2,z3,z4)
x=cbind(x1,x2,x3,x4)
#missing data
y[tr==0] <- 0
#logistic propensity score model, correct
ppi.glm <- glm(tr~z, family=binomial(link=logit))
X <- model.matrix(ppi.glm)
ppi.hat <- ppi.glm$fitted
#ppi.hat treated as known
out.HT <- mn.HT(y, tr, ppi.hat)
out.HT$mu
out.HT$v
#ppi.hat treated as estimated
out.HT <- mn.HT(y, tr, ppi.hat, X)
out.HT$mu
out.HT$v
#balance checking
out.HT <- mn.HT(x, tr, ppi.hat, X, bal=TRUE)
out.HT$mu
out.HT$v
out.HT$mu/ sqrt(out.HT$v) #t-statistic
``` |

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