Description Usage Arguments Details Value References Examples
Estimate average treatment effect (ATE) with augmented inverse propensity score weighting.
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data |
a dataframe object containing the variables and values. |
x |
a list of character vectors specifying variables to be included in the model (columns in the data). If unspecified, then it is assumed to be all columns in the data besides y and w. |
y |
a character vector specifying the response variable. |
w |
a character vector specifying the treatment status. |
p |
a vector containing propensity score values. |
cf |
logical; if TRUE then includes confidence interval on ATE. |
Estimates the ATE τ using the doubly robust method described in Scharfstein, Robins and Rotznitzky (1998) that combines both regression and propensity score weighting.
τ = E ≤ft[ W_i \frac{Y_i-τ(1,X_i)}{e(X_i)} + (1-W_i) \frac{Y_i-τ(0,X_i)}{1-e(X_i)} + τ(1,X_i) - τ(0,X_i)\right]
where e(X_i) = P(W_i = 1 | X_i = x) is the propensity score and τ(1, X_i) and τ{0, X_i} are estimated in the first stage via OLS regression.
a list of ATE, 95 percent confidence interval upperbound and lowerbound or just ATE, depending on user input of cf
.
Rotnitzky, Andrea, James M. Robins, and Daniel O. Scharfstein. 1998. “Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse." Journal of the American Statistical Association. Vol. 93, No. 444, Dec. pgs. 1321-1339.
Cao, Weihua, Anastasios A. Tsiatis, and Marie Davidian. 2009. “Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data". Biometrika. Vol. 96(3). pgs. 723–734.
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