# Zelig - error in ATT calculate

I'm trying to calculate the Average Treatment Effect on the Treated (TT) using Zelig package following the paper "MatchIt: Nonparametric Preprocessing for
Parametric Causal Inference", available in https://imai.fas.harvard.edu/research/files/matchit.pdf. However, in the last part (s.out function), when I go to calculate the ATT, show the follow error : Error in eigen (Sigma, symmetric = TRUE) : infinite or missing values in 'x'. How can I solve it?

``````library(Zelig)

data(lalonde)

m.out <- matchit(treat ~ educ + age + black + hisp + married + nodegr + re74 + re75, data = lalonde, method = "nearest", ratio = 1)

z.out <- zelig (re78 ~ treat + age + educ + black + nodegr + hisp + married + re74 + re75,
data = match.data(m.out, "control")  , model = "ls")

x.out <- setx(z.out, data = match.data(m.out, "treat"), cond = TRUE)

s.out <- sim(z.out, x = x.out)
``````

Thanks so much.

Júlio

Welcome to the forum Júlio. The short answer is, remove `treat` from the `zelig` model formula. You're fitting the model only for the matched control observations, that is, those observations that, in this case, all have `treat=0`. As a result, `treat` shouldn't be an independent variable in the model. See below for more details.

I describe below how to make the ATT calculation work, but first, to make your example run, we need to make a few changes to your code: We'll need the following packages:

``````library(Zelig)
library(MatchIt)
``````

We also need the correct variable names for the model specification. `hisp` should be `hispan` and `nodegr` should be `nodegree`.

Now, to address the error you're getting: In `z.out` you have (after making the corrections described above):

``````z.out <- zelig (re78 ~ treat + age + educ + black + nodegree + hispan + married + re74 + re75,
data = match.data(m.out, "control")  , model = "ls")
``````

This results in the model being run only with matched data rows that have `treat=0`. Yet `treat` is also included in the model formula. Since `treat` has only one value, no coefficient for `treat` is estimated in the model. That's what is ultimately causing the error you're getting.

Here's what I get for `z.out` when I run your code (with the changes described at the beginning):

``````Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.714e+03  4.246e+03  -0.875   0.3830
treat               NA         NA      NA       NA
age         -1.791e+01  4.624e+01  -0.387   0.6991
educ         6.774e+02  2.634e+02   2.572   0.0109
black        1.444e+02  1.118e+03   0.129   0.8974
nodegree     2.435e+03  1.395e+03   1.746   0.0826
hispan       1.529e+03  1.294e+03   1.182   0.2390
married     -1.197e+03  1.203e+03  -0.995   0.3210
re74         1.574e-02  1.348e-01   0.117   0.9072
re75         4.253e-01  2.079e-01   2.046   0.0423
``````

As described on page 12-13 of the `MatchIt` vignette, to calculate the Average Treatment Effect on the Treated (ATT), we fit the model just for the matched control group observations, which is what you've done. However, we need to exclude `treat` from the `z.out` model formula, since `treat` has only one value. If the matching procedure has controlled for selection bias, then this model gives us the counterfactual (what `re78` would be for the treated group if it had not been treated). Then we apply the coefficients from this model to the matched treated observations (the matched observations for which `treat=1`) to get the ATT.

``````library(Zelig)
library(MatchIt)

m.out <- matchit(treat ~ educ + age + black + hispan + married + nodegree + re74 + re75,
data = lalonde, method = "nearest", ratio = 1)

z.out <- zelig(re78 ~ age + educ + black + nodegree + hispan + married + re74 + re75,
data = match.data(m.out, "control"), model = "ls")

x.out <- setx(z.out, data = match.data(m.out, "treat"), cond = TRUE)

s.out <- sim(z.out, x = x.out)

m.out
#>
#> Call:
#> matchit(formula = treat ~ educ + age + black + hispan + married +
#>     nodegree + re74 + re75, data = lalonde, method = "nearest",
#>     ratio = 1)
#>
#> Sample sizes:
#>           Control Treated
#> All           429     185
#> Matched       185     185
#> Unmatched     244       0

z.out
#> Model:
#>
#> Call:
#> z5\$zelig(formula = re78 ~ age + educ + black + nodegree + hispan +
#>     married + re74 + re75, data = match.data(m.out, "control"))
#>
#> Residuals:
#>    Min     1Q Median     3Q    Max
#>  -9411  -4362  -1854   2639  17392
#>
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -3.714e+03  4.246e+03  -0.875   0.3830
#> age         -1.791e+01  4.624e+01  -0.387   0.6991
#> educ         6.774e+02  2.634e+02   2.572   0.0109
#> black        1.444e+02  1.118e+03   0.129   0.8974
#> nodegree     2.435e+03  1.395e+03   1.746   0.0826
#> hispan       1.529e+03  1.294e+03   1.182   0.2390
#> married     -1.197e+03  1.203e+03  -0.995   0.3210
#> re74         1.574e-02  1.348e-01   0.117   0.9072
#> re75         4.253e-01  2.079e-01   2.046   0.0423
#>
#> Residual standard error: 5910 on 176 degrees of freedom
#> Multiple R-squared:  0.09413,    Adjusted R-squared:  0.05296
#> F-statistic: 2.286 on 8 and 176 DF,  p-value: 0.02365
#>
#> Next step: Use 'setx' method

x.out
#> setx:
#>   (Intercept)  age educ black nodegree hispan married re74 re75
#> 1           1 25.3 10.6  0.47    0.638  0.216   0.211 2342 1615
#>
#> Next step: Use 'sim' method

s.out
#>
#>  sim x :
#>  -----
#> ev
#>       mean       sd      50%     2.5%    97.5%
#> 1 5437.778 425.0799 5432.914 4611.991 6227.689
#> pv
#>         mean       sd      50%      2.5%    97.5%
#> [1,] 5285.28 5799.457 5368.345 -6447.408 15900.29
``````

Created on 2019-07-13 by the reprex package (v0.3.0)

Dear Joels,

Thank you so much for your help. Yow were very careful with me! The problem was simple. I looked for the error before, but I didn't find... Just now I see!!!! However I still have a doubt: the sim command give the the values of ev and pv. I guess the ATT effect is the first difference (fd) between their means . But the command don't give me it. Do you know how to give this value? Thanks again!

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