Estimator for the Average Potential Outcome

Estimator for the average potential outcome, allowing for custom weights $\omega(X)$.

Usage

ddml_apo(
  y,
  D,
  X,
  d = 1,
  weights = NULL,
  learners,
  learners_DX = learners,
  sample_folds = 10,
  ensemble_type = "nnls",
  shortstack = FALSE,
  cv_folds = 10,
  custom_ensemble_weights = NULL,
  custom_ensemble_weights_DX = custom_ensemble_weights,
  cluster_variable = seq_along(y),
  stratify = TRUE,
  trim = 0.01,
  silent = FALSE,
  parallel = NULL,
  fitted = NULL,
  splits = NULL,
  save_crossval = TRUE,
  ...
)

Arguments

y

The outcome variable.

D

The endogenous variable of interest. Can be discrete or continuous.

X

A (sparse) matrix of control variables.

d

The treatment level of interest. The default is d = 1.

weights

A numeric vector of length nobs specifying the weights $\omega(X)$. If weights = NULL (the default), a vector of 1s is used, which estimates the Average Potential Outcome (APO).

learners

May take one of two forms, depending on whether a single learner or stacking with multiple learners is used for estimation of the conditional expectation functions. If a single learner is used, learners is a list with two named elements:

what The base learner function. The function must be such that it predicts a named input y using a named input X.
args Optional arguments to be passed to what.

If stacking with multiple learners is used, learners is a list of lists, each containing three named elements:

what The base learner function. The function must be such that it predicts a named input y using a named input X.
args Optional arguments to be passed to what.
assign_X An optional vector of column indices corresponding to control variables in X that are passed to the base learner.

Omission of the args element results in default arguments being used in what. Omission of assign_X results in inclusion of all variables in X.

learners_DX

Optional argument to allow for different estimators of $E[D|X]$. Setup is identical to learners.

sample_folds

Number of cross-fitting folds.

ensemble_type

Ensemble method to combine base learners into final estimate of the conditional expectation functions. Possible values are:

"nnls" Non-negative least squares.
"nnls1" Non-negative least squares with the constraint that all weights sum to one.
"singlebest" Select base learner with minimum MSPE.
"ols" Ordinary least squares.
"average" Simple average over base learners.

Multiple ensemble types may be passed as a vector of strings.

shortstack

Boolean to use short-stacking.

cv_folds

Number of folds used for cross-validation in ensemble construction.

custom_ensemble_weights

A numerical matrix with user-specified ensemble weights. Each column corresponds to a custom ensemble specification, each row corresponds to a base learner in learners (in chronological order). Optional column names are used to name the estimation results corresponding the custom ensemble specification.

custom_ensemble_weights_DX

Optional argument to allow for different custom ensemble weights for learners_DX. Setup is identical to custom_ensemble_weights. Note: custom_ensemble_weights and custom_ensemble_weights_DX must have the same number of columns.

cluster_variable

A vector of cluster indices.

stratify

Boolean for stratified cross-fitting: if TRUE, subsamples are constructed to be balanced across treatment levels.

trim

Number in (0, 1) for trimming the estimated propensity scores at trim and 1-trim.

silent

Boolean to silence estimation updates.

parallel

An optional named list with parallel processing options. When NULL (the default), computation is sequential. Supported fields:

cores: Number of cores to use.
export: Character vector of object names to export to parallel workers (for custom learners that reference global objects).
packages: Character vector of additional package names to load on workers (for custom learners that use packages not imported by ddml).

fitted

An optional named list of per-equation cross-fitted predictions, typically obtained from a previous fit via fit$fitted. When supplied (together with splits), base learners are not re-fitted; only ensemble weights are recomputed. This allows fast re-estimation with a different ensemble_type. See ddml_plm for an example.

splits

An optional list of sample split objects. For ddml_apo, this must be a list with elements subsamples and cv_subsamples (and optionally subsamples_byD and cv_subsamples_byD for stratified splitting). Typically obtained from a previous fit via fit$splits.

save_crossval

Logical indicating whether to store the inner cross-validation residuals used for ensemble weight computation. Default TRUE. When TRUE, subsequent pass-through calls with data-driven ensembles (e.g., "nnls") reproduce per-fold weights exactly. Set to FALSE to reduce object size at the cost of approximate weight recomputation.

...

Additional arguments passed to internal methods.

Value

ddml_apo returns an object of S3 class ddml_apo and ddml. See ddml-intro for the common output structure. Additional pass-through fields: learners, learners_DX.

Details

Parameter of Interest: ddml_apo provides a Double/Debiased Machine Learning estimator for the average potential outcome. Under conditional unconfoundedness and overlap, the parameter is identified by the following reduced form conditional expectation:

$$\theta_0^{\textrm{APO}} = E[\omega(X) E[Y|D=d, X]],$$

where $W \equiv (Y, D, X)$ is the observed random vector and $\omega(X)$ is a known weighting function. If $\omega(X) = 1$, this parameter corresponds to the average potential outcome at treatment level $d$.

Nuisance Parameters: The nuisance parameters are $\eta = (\ell, r)$ taking true values $\ell_0(X) = E[Y|D=d, X]$ and $r_0(X) = \Pr(D=d|X)$.

Neyman Orthogonal Score / Moment Equation: The Neyman orthogonal score is:

$$m(W; \theta, \eta) = \left( \frac{\mathbf{1}\{D=d\} (Y - \ell(X))}{r(X)} + \ell(X) \right) \omega(X) - \theta$$

Jacobian:

$$J = -1$$

See ddml-intro for how the influence function and inference are derived from these components.

Examples

# Construct variables from the included Angrist & Evans (1998) data
y = AE98[, "worked"]
D = AE98[, "morekids"]
X = AE98[, c("age","agefst","black","hisp","othrace","educ")]

# Estimate the APO for d = 1 using a single base learner, ridge.
apo_fit <- ddml_apo(y, D, X,
                    learners = list(what = mdl_glmnet),
                    sample_folds = 2,
                    silent = TRUE)
summary(apo_fit)
#> DDML estimation: Average Potential Outcome 
#> Obs: 5000   Folds: 2
#> 
#>     Estimate Std. Error z value Pr(>|z|)    
#> APO   0.4352     0.0125    34.8   <2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1