dmlx

Double machine learning by cross-fitting

R

dmlx(
  y,
  d,
  x,
  model = "plr",
  fold,
  z = NULL,
  s = NULL,
  t = NULL,
  score = NULL,
  lrn = "lasso",
  lrnd = lrn,
  alpha = 0.5,
  nlam = 100,
  nfin = 5,
  ntree = 500,
  mtry = NULL,
  trim = NULL,
  clu = NULL,
  pred = NULL,
  seed = 1
)

Estimates a causal parameter from orthogonal scores whose nuisance functions (outcome regressions, propensities) are learned out of fold: each fold’s predictions come from learners fitted on the other folds, so that the estimate keeps root-n inference while the learners are flexible (Chernozhukov et al. 2018). The learning, the scores, the estimate and its standard error are all compiled; the folds are the caller’s, so results can be matched with other software fold for fold.

Parameters

y

The outcome: one number per row of x (for "did", the change between the periods; for "ssm", NA allowed where s is 0).

d

The treatment: numbers ("plr", "pliv") or 0 and 1.

x

The covariates: a numeric matrix, one row per observation.

model

One of "plr", "pliv", "irm", "iivm", "aipw", "tmle", "did", "didcs", "ssm".

fold

The fold of each row, whole numbers from 1 to the number of folds (at least 2), each used; or a matrix of them, one column per repetition of the cross-fitting.

z

The instrument ("pliv"; 0 and 1 for "iivm"), else NULL.

s

For "ssm", 1 where the outcome is observed and 0 where not.

t

For "didcs", the period of each row, 0 or 1.

score

NULL for the default ("po", or "ate"), or "po" or "iv" for "plr" and "pliv", "ate" or "atte" for "irm".

lrn

The learner of the outcome nuisances: "glm" (least squares; logistic regression for a probability), "lasso", "ridge" or "enet" (the elastic net of enpath() with mixing alpha, the penalty chosen by cross-validation within the training folds), or "forest" (the random forest of rfor() with ntree trees and mtry columns per split, nodes of 5 rows, or 10 for a probability, left unsplit).

lrnd

The learner of the treatment, instrument and selection nuisances, from the same choices.

alpha

The mixing parameter of "enet", from 0 to 1.

nlam

The number of penalties on each elastic net path.

nfin

The number of inner folds that choose the penalty, at least 2.

ntree

The number of trees of "forest": a whole number, at least 1.

mtry

The columns tried at each split of "forest": NULL for the floor of the square root of the number of columns, or a whole number (more than the columns means all of them).

trim

Propensities are kept within trim of 0 and 1: NULL for the default of the reference implementation (1e-12 for "irm", "iivm" and "ssm", as ‘DoubleML’; 0.01 for "did" and "didcs", as ‘DoubleML’ for ‘Python’; 0.025 for "aipw", as ‘AIPW’; for "tmle", 5 / sqrt(n) / log(n), a lower bound only, as ‘tmle’).

clu

NULL, or the cluster of each row (whole numbers from 1 to the number of clusters, each used), for "plr" and "pliv"; folds must then be constant within clusters.

pred

NULL to learn the nuisances, or their predictions: a matrix with one row per observation and one column per nuisance, in the order listed above (an array with a third dimension for several repetitions). The estimate is then a closed form of these.

seed

The seed of the inner folds and of the forests: one number, at least 0.

Returns

A list: coef and se, the estimate and its standard error; reps, a data frame with each repetition’s coef and se; psia and psib, the score elements (one column per repetition; the estimate solves mean(psia * coef + psib) = 0); pred, the out-of-fold predictions, an array named by nuisance.

Notes

The models are those of ‘DoubleML’, with its scores and its defaults:

• "plr", the partially linear regression y = theta d + g(x) + e, score "po" (partialling out: nuisances l = E[y | x], m = E[d | x]) or "iv" (IV-type: also g, learned on y - theta0 d with theta0 from the partialling-out score);
• "pliv", the partially linear IV model with one instrument z (nuisances l, m = E[z | x], r = E[d | x], and g for "iv");
• "irm", the interactive regression model for a 0/1 treatment, score "ate" or "atte" (nuisances g0, g1, the outcome regressions among the untreated and the treated, and the propensity m);
• "iivm", the interactive IV model (local average effect) for a 0/1 treatment and a 0/1 instrument (g0, g1, m = P(z = 1 | x), r0, r1, the treatment probabilities given z);
• "did", difference in differences on a panel: y is the change in the outcome, d the 0/1 group (nuisances g0, the change among the untreated, and m), with the in-sample normalised observational score of ‘DoubleML’ for ‘Python’ (Sant’Anna and Zhao 2020; Chang 2020);
• "didcs", the same on repeated cross-sections, t the 0/1 period (nuisances g00, g01, g10, g11, the outcome regressions in each group and period, and m);
• "ssm", the average effect when the outcome is observed only where s is 1, missing at random given d and x (Bia, Huber and Laffers 2023; nuisances pi = P(s = 1 | d, x), m, g0, g1);
• "aipw" and "tmle", the average effect of a 0/1 treatment from the nuisances of "irm": augmented inverse probability weighting, with the standard error of the ‘AIPW’ package (the standard deviation of the influence function), and targeted maximum likelihood with the fluctuation of the ‘tmle’ package (Gruber and van der Laan 2012).

The estimate solves the mean of the score over all folds (DoubleML’s "dml2"); its variance is mean(psi^2) / mean(psi_a)^2 / n, or, with clusters (one-way, models "plr" and "pliv"), the cluster-robust form of Chiang et al. (2022). With several repetitions of the folds (columns of fold), the estimate is the median over repetitions and the variance the median of var + (theta_r - theta)^2.

References

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. and Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. doi:10.1111/ectj.12097

Chiang, H. D., Kato, K., Ma, Y. and Sasaki, Y. (2022). Multiway cluster robust double/debiased machine learning. Journal of Business and Economic Statistics, 40(3), 1046-1056. doi:10.1080/07350015.2021.1895815

Sant’Anna, P. H. C. and Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219(1), 101-122. doi:10.1016/j.jeconom.2020.06.003

Chang, N.-C. (2020). Double/debiased machine learning for difference-in-differences models. The Econometrics Journal, 23(2), 177-191. doi:10.1093/ectj/utaa001

Bia, M., Huber, M. and Laffers, L. (2023). Double machine learning for sample selection models. Journal of Business and Economic Statistics, 42(3), 958-969. doi:10.1080/07350015.2023.2271071

Gruber, S. and van der Laan, M. J. (2012). tmle: an R package for targeted maximum likelihood estimation. Journal of Statistical Software, 51(13), 1-35. doi:10.18637/jss.v051.i13

Examples

set.seed(1)
n <- 500
x <- matrix(rnorm(n * 5), n)
d <- x[, 1] + rnorm(n)
y <- 0.5 * d + x[, 1] - x[, 2] + rnorm(n)
fold <- sample(rep(1:5, length.out = n))
r <- dmlx(y, d, x, "plr", fold, lrn = "glm", lrnd = "glm")
c(r$coef, r$se)

Generated from the R help page: dmlx in the R reference.