Question

我在R中使用Match（）库，我需要AT的CI。

有办法搞定吗？我想在计算ATT和CI时使用倾向得分。
如何计算？（即什么是公式？为什么？）

干杯，

https://stats.stackexchange.com/questions/132509/confidence-interval-for-average-treatment-effect-from-propensity-score-weighting

和：https://stats.stackexchange.com/questions/238431/is-the-average-treatment-effect-on-the-treated-att-a-meaningful-comparison-in

PS2：附上相关的代码;在找到平衡之后，我尝试使用回归+ confint（）方法获得CI，但它不起作用，因为我不知道如何通过倾向得分，我强迫进入回归模型（我确定这是不必要的，但我只知道CI的配置功能。）

(3) Using the Match() help file code example as a guide, use propensity score matching to produce an estimated treatment effect and confidence interval. Report your results.

```{r}
library(Matching)

DataFrame=as.data.frame(data1)

# Estimate the propensity model

glm1  <- glm(treat~age + I(age^2) + education + I(education^2) + black +
             hispanic + married + nodegree + re74  + I(re74^2) + re75 + I(re75^2) , family=binomial, data=DataFrame)
#save data objects
X  <- glm1$fitted
Y  <- DataFrame$re78
Tr  <- DataFrame$treat

# One-to-one matching with replacement (the "M=1" option).
# Estimating the treatment effect on the treated (the "estimand" option defaults to ATT==Average Treatment effect for Treated).
rr  <- Match(Y=Y, Tr=Tr, X=X, M=1);
summary(rr)
```
Finding Balance:

```{r}
# Let's check the covariate balance:
mb  <- MatchBalance(treat~age + I(age^2) + education + I(education^2) + black +hispanic + married + nodegree + re74  + I(re74^2) + re75 + I(re75^2), data=DataFrame, match.out=rr, nboots=500)

rr1  <- Match(Y=Y, Tr=Tr, X=X, M=1,Weight.matrix=);

#After obtaining balance, find ATT 
rr1  <- Match(Y=Y, Tr=Tr, X=X, M=1);
summary(rr1)
```

Find a way to obtain CIs - Doesnt work:
```{r}
X<-mb
Y<-re78
RegressionOnMatched<-lm(re78~X,data = )
confint(RegressionOnMatched)
#mean(rr$re78)
#quantile(rr$re78, c(0.025, 0.975))
```

Answer 1

我相信你的问题是你传递给分位数的论点。试试这个：

ci_upper <- 2*mean(rr$re78) - quantile(rr$re78, 0.025)    
ci_lower <- 2*mean(rr$re78) - quantile(rr$re78, 0.975)
ci <- c(ci_lower, ci_upper)

如何使用Match（）获取ATT的置信区间

1 个答案: