In this document, we demonstrate some common uses of
`Zelig`

(Imai, King, and Lau
2008) and how the same tasks can be performed using
`clarify`

. We’ll include examples for computing predictions
at representative values (i.e., `setx()`

and
`sim()`

in `Zelig`

), the rare-events logit model,
estimating the average treatment effect (ATT) after matching, and
combining estimates after multiple imputation.

The usual workflow in `Zelig`

is to fit a model using
`zelig()`

, specify quantities of interest to simulate using
`setx()`

on the `zelig()`

output, and then
simulate those quantities using `sim()`

. `clarify`

uses a similar approach, except that the model is fit outside
`clarify`

using functions in a different R package. In
addition, `clarify`

’s `sim_apply()`

allows for the
computation of any arbitrary quantity of interest. Unlike
`Zelig`

, `clarify`

follows the recommendations of
Rainey (2023) to use the estimates
computed from the original model coefficients rather than the average of
the simulated draws. We’ll demonstrate how to replicate a standard
`Zelig`

analysis using `clarify`

step-by-step.
Because simulation-based inference involves randomness and some of the
algorithms may not perfectly align, one shouldn’t expect results to be
identical, though in most cases, they should be similar.

Note that both `Zelig`

and `clarify`

have a
function called “`sim()`

”, so we will always make it clear
which package’s `sim()`

is being used.

Here we’ll use the `lalonde`

dataset in
`{MatchIt}`

and fit a linear model for `re78`

as a
function of the treatment `treat`

and covariates.

We’ll be interested in the predicted values of the outcome for a typical unit at each level of treatment and their first difference.

`Zelig`

workflowIn `Zelig`

, we fit the model using
`zelig()`

:

```
fit <- zelig(re78 ~ treat + age + educ + married + race +
nodegree + re74 + re75, data = lalonde,
model = "ls", cite = FALSE)
```

Next, we use `setx()`

and `setx1()`

to set our
values of `treat`

:

Next we simulate the values using `sim()`

:

Finally, we can print and plot the predicted values and first differences:

`clarify`

workflowIn `clarify`

, we fit the model using functions outside
`clarify`

, like `stats::lm()`

or
`fixest::feols()`

.

Next, we simulate the model coefficients using
`clarify::sim()`

:

Next, we use `sim_setx()`

to set our values of the
predictors:

Finally, we can summarize and plot the predicted values:

`Zelig`

uses a special method for logistic regression with
rare events as described in King and Zeng
(2001). This is the primary implementation of the method in R.
However, newer methods have been developed that perform similarly to or
better than the method of King and Zeng (Puhr et
al. 2017) and are implemented in R packages that are compatible
with `clarify`

, such as `logistf`

and
`brglm2`

.

Here, we’ll use the `lalonde`

dataset with a constructed
rare outcome variable to demonstrate how to perform a rare events
logistic regression in `Zelig`

and in
`clarify`

.

```
data("lalonde", package = "MatchIt")
#Rare outcome: 1978 earnings over $20k; ~6% prevalence
lalonde$re78_20k <- lalonde$re78 >= 20000
```

`Zelig`

workflowIn `Zelig`

, we fit a rare events logistic model using
`zelig()`

with `model = "relogit"`

.

```
fit <- zelig(re78_20k ~ treat + age + educ + married + race +
nodegree + re74 + re75, data = lalonde,
model = "relogit", cite = FALSE)
fit
```

We can compute predicted values at representative values using
`setx()`

and `Zelig::sim()`

as above.

`clarify`

workflowHere, we’ll use `logistf::logistif()`

with
`flic = TRUE`

, which performs a variation on Firth’s logistic
regression with a correction for bias in the intercept (Puhr et al. 2017).

```
fit <- logistf::logistf(re78_20k ~ treat + age + educ + married + race +
nodegree + re74 + re75, data = lalonde,
flic = TRUE)
summary(fit)
#> logistf::logistf(formula = re78_20k ~ treat + age + educ + married +
#> race + nodegree + re74 + re75, data = lalonde, flic = TRUE)
#>
#> Model fitted by Penalized ML
#> Coefficients:
#> coef se(coef) lower 0.95 upper 0.95 Chisq
#> (Intercept) -9.313465e+00 2.036981e+00 -1.330595e+01 -5.3209820512 24.4427501
#> treat 1.106769e+00 6.165332e-01 -1.029333e-01 2.3139987641 3.2185792
#> age 4.144350e-02 2.147498e-02 -8.432745e-04 0.0825427498 3.6937075
#> educ 2.936673e-01 1.283624e-01 4.612509e-02 0.5477908006 5.4633187
#> married 2.909164e-01 4.769332e-01 -6.309243e-01 1.2240602694 0.3844676
#> racehispan 1.056388e+00 7.276772e-01 -4.150796e-01 2.4305568008 2.0440063
#> racewhite 5.431919e-01 6.160352e-01 -6.255328e-01 1.7838676828 0.8008622
#> nodegree 2.868319e-01 5.852738e-01 -8.531608e-01 1.4243907533 0.2462839
#> re74 1.108995e-04 2.821452e-05 5.775266e-05 0.0001664918 17.0427331
#> re75 4.457916e-05 4.720736e-05 -4.706292e-05 0.0001334568 0.9472092
#> p method
#> (Intercept) 7.655103e-07 1
#> treat 7.280680e-02 2
#> age 5.461808e-02 2
#> educ 1.941973e-02 2
#> married 5.352219e-01 2
#> racehispan 1.528067e-01 2
#> racewhite 3.708357e-01 2
#> nodegree 6.197040e-01 2
#> re74 3.654797e-05 2
#> re75 3.304307e-01 2
#>
#> Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
#>
#> Likelihood ratio test=65.80085 on 9 df, p=1.007627e-10, n=614
#> Wald test = 179.4243 on 9 df, p = 0
```

We can compute predictions at representative values using
`clarify::sim()`

and `sim_setx()`

.

Here we’ll use the `lalonde`

dataset and perform
propensity score matching and then fit a linear model for
`re78`

as a function of the treatment `treat`

, the
covariates, and their interaction. From this model, we’ll compute the
ATT of `treat`

using `Zelig`

and
`clarify`

.

```
data("lalonde", package = "MatchIt")
m.out <- MatchIt::matchit(treat ~ age + educ + married + race +
nodegree + re74 + re75, data = lalonde,
method = "nearest")
```

`Zelig`

workflowIn `Zelig`

, we fit the model using `zelig()`

directly on the `matchit`

object:

```
fit <- zelig(re78 ~ treat * (age + educ + married + race +
nodegree + re74 + re75),
data = m.out, model = "ls", cite = FALSE)
```

Next, we use `ATT()`

to request the ATT of
`treat`

and simulate the values:

`clarify`

workflowIn `clarify`

, we need to extract the matched dataset and
fit a model outside `clarify`

using another package.

```
m.data <- MatchIt::match.data(m.out)
fit <- lm(re78 ~ treat * (age + educ + married + race +
nodegree + re74 + re75),
data = m.data)
```

Next, we simulate the model coefficients using
`clarify::sim()`

. Because we performed pair matching, we will
request a cluster-robust standard error:

Next, we use `sim_ame()`

to request the average marginal
effect of `treat`

within the subset of treated units:

Finally, we can summarize and plot the ATT:

Here we’ll use the `africa`

dataset in
`{Amelia}`

to demonstrate combining estimates after multiple
imputation. This analysis is also demonstrated using
`clarify`

at the end of `vignette("clarify")`

.

First we multiply impute the data using `amelia()`

using
the specification in the `{Amelia}`

documentation.

```
# Multiple imputation
a.out <- amelia(x = africa, m = 10, cs = "country",
ts = "year", logs = "gdp_pc", p2s = 0)
```

`Zelig`

workflowWith `Zelig`

, we can supply the `amelia`

object
directly to the `data`

argument of `zelig()`

to
fit a model in each imputed dataset:

Summarizing the coefficient estimates after the simulation can be
done using `summary()`

:

We can use `Zelig::sim()`

and `setx()`

to
compute predictions at specified values of the predictors:

```
fit <- setx(fit, infl = 0, trade = 40)
fit <- setx1(fit, infl = 0, trade = 60)
fit <- Zelig::sim(fit)
```

`Zelig`

does not allow you to combine predicted values
across imputations.

`clarify`

workflow`clarify`

does not combine coefficients, unlike
`zelig()`

; instead, the models should be fit using
`Amelia::with()`

. To view the combined coefficient estimates,
use `Amelia::mi.combine()`

.

```
#Use Amelia functions to model and combine coefficients
fits <- with(a.out, lm(gdp_pc ~ infl * trade))
mi.combine(fits)
#> # A tibble: 4 × 8
#> term estimate std.error statistic p.value df r miss.info
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -177. 117. -1.51 1.87e+ 0 130515. 0.00837 0.00832
#> 2 infl 12.3 9.36 1.31 1.91e- 1 1210309. 0.00273 0.00273
#> 3 trade 21.3 1.82 11.7 1.26e-31 83428. 0.0105 0.0104
#> 4 infl:trade -0.290 0.140 -2.07 1.96e+ 0 954984. 0.00308 0.00307
```

Derived quantities can be computed using
`clarify::misim()`

and `sim_apply()`

or its
wrappers on the `with()`

output, which is a list of
regression model fits:

```
#Simulate coefficients, 100 in each of 10 imputations
s <- misim(fits, n = 100)
#Compute predictions at specified values
est <- sim_setx(s, x = list(infl = 0, trade = 40),
x1 = list(infl = 0, trade = 60),
verbose = FALSE)
summary(est)
#> Estimate 2.5 % 97.5 %
#> trade = 40 675 570 784
#> trade = 60 1101 1024 1174
#> FD 426 352 498
plot(est)
```

Imai, Kosuke, Gary King, and Olivia Lau. 2008. “Toward a Common
Framework for Statistical Analysis and Development.” *Journal
of Computational and Graphical Statistics* 17 (4): 892–913. https://doi.org/10.1198/106186008X384898.

King, Gary, and Langche Zeng. 2001. “Logistic Regression in Rare
Events Data.” *Political Analysis* 9 (2): 137–63. https://doi.org/10.1093/oxfordjournals.pan.a004868.

Puhr, Rainer, Georg Heinze, Mariana Nold, Lara Lusa, and Angelika
Geroldinger. 2017. “Firth’s Logistic Regression with Rare Events:
Accurate Effect Estimates and Predictions?” *Statistics in
Medicine* 36 (14): 2302–17. https://doi.org/10.1002/sim.7273.

Rainey, Carlisle. 2023. “A Careful Consideration of
CLARIFY: Simulation-Induced Bias in Point Estimates of
Quantities of Interest.” *Political Science Research and
Methods*, April, 1–10. https://doi.org/10.1017/psrm.2023.8.