## Introduction

This article illustrates the computational advantages of short-stacking over conventional stacking for estimation of structural parameters using double/debiased machine learning. See also Ahrens et al. (2024a, 2024b) for further discussion of short-stacking.

## Estimation with Stacking and Short-Stacking

We apply `ddml`

to the included random subsample of 5,000
observations from the data of Angrist & Evans (1998). The data
contains information on the labor supply of mothers, their children, as
well as demographic data. See `?AE98`

for details.

```
# Load ddml and set seed
library(ddml)
set.seed(221945)
# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
D = AE98[, "morekids"]
Z = AE98[, "samesex"]
X = AE98[, c("age","agefst","black","hisp","othrace","educ")]
```

For a comparison of run-times, we consider the following three estimators for the nuisance parameters arising in estimation of the local average treatment effect (LATE):

- Gradient boosting as the single base learner (see
`?mdl_xgboost`

) - Short-stacking with linear regression (see
`?ols`

), lasso (see`?mdl_glmnet`

), and gradient boosting - Stacking with linear regression, lasso, and gradient boosting

```
time_singlelearner <- system.time({
late_fit <- ddml_late(y, D, Z, X,
learners = list(what = mdl_xgboost,
args = list(nrounds = 100,
max_depth = 1)),
sample_folds = 10,
silent = TRUE)
})#SYSTEM.TIME
time_shortstacking <- system.time({
late_fit <- ddml_late(y, D, Z, X,
learners = list(list(fun = ols),
list(fun = mdl_glmnet),
list(fun = mdl_xgboost,
args = list(nrounds = 100,
max_depth = 1))),
ensemble_type = 'nnls1',
shortstack = TRUE,
sample_folds = 10,
silent = TRUE)
})#SYSTEM.TIME
time_stacking <- system.time({
late_fit <- ddml_late(y, D, Z, X,
learners = list(list(fun = ols),
list(fun = mdl_glmnet),
list(fun = mdl_xgboost,
args = list(nrounds = 100,
max_depth = 1))),
ensemble_type = 'nnls1',
shortstack = FALSE,
sample_folds = 10,
cv_folds = 10,
silent = TRUE)
})#SYSTEM.TIME
```

Both stacking and short-stacking construct weighted averages of the considered base learners to minimize the out-of-sample mean squared prediction error (MSPE). The difference between the two approaches lies in the construction of the MSPE: While stacking runs cross-validation in each cross-fitting sample fold, short-stacking directly uses the out-of-sample predictions arising in the cross-fitting step of double/debiased machine learning estimators. As the run-times below show, this results in a substantially reduced computational burden:

## References

Ahrens A, Hansen C B, Schaffer M E, Wiemann T (2024a). “ddml: Double/debiased machine learning in Stata.” Stata Journal, 24(1): 3-45.

Ahrens A, Hansen C B, Schaffer M E, Wiemann T (2024b). “Model averaging and double machine learning.” https://arxiv.org/abs/2401.01645

Angrist J, Evans W (1998). “Children and Their Parents’ Labor Supply: Evidence from Exogenous Variation in Family Size.” American Economic Review, 88(3), 450-477.