oalasso

Outcome-adaptive lasso propensity scores — published methods, exactly.

oalasso implements the outcome-adaptive lasso (OAL) of Shortreed & Ertefaie (2017) and the generalized outcome-adaptive lasso (GOAL) of Baldé, Yang & Lefebvre (2023) for propensity score estimation. OAL fits a logistic propensity score model with an adaptively weighted lasso penalty whose weights come from an outcome regression: covariates unrelated to the outcome — instruments and noise variables — receive an exploding penalty and are excluded, while confounders and outcome predictors are retained. Excluding instruments from a propensity score model improves precision and avoids bias amplification under unmeasured confounding (Brookhart et al. 2006; Myers et al. 2011). Tuning is by the weighted absolute mean difference (wAMD) balance criterion, and method = "goal" adds Baldé’s elastic-net ridge term for correlated covariates and fragile positivity.

Fidelity is the package’s identity. The solver is glmnet with an exact, KKT-verified penalty-scale correction, so the published objectives and tuning grids (Shortreed & Ertefaie’s $\lambda_n = n^\delta$ grid with paired $\gamma$) are reproduced rather than silently reinterpreted by glmnet’s internal penalty-factor rescaling. Every fit prints a provenance line stating exactly which published method it implements — and flags anything that goes beyond the validated territory. The pipeline is fully deterministic (no seed needed).

oalasso is the second package of a suite with psAve, and a companion to the MatchIt/WeightIt/cobalt ecosystem. The deliverable is a plain numeric vector of propensity scores: fit$ps drops into MatchIt::matchit() as a distance measure, WeightIt::weightit() as a propensity score, or psAve::psave() as an appended candidate; thin oal_match() / oal_weight() wrappers do this without retyping the formula, and cobalt::bal.tab() works on the fitted object directly. Effect estimation stays where it belongs, in MatchIt/WeightIt/survey/marginaleffects.

Installation

# install.packages("remotes")
remotes::install_github("kabajiro/oalasso")

Quick example

library(oalasso)
data("lalonde", package = "MatchIt")

fit <- oal(treat ~ age + educ + race + married + nodegree + re74 + re75,
           data = lalonde, outcome = ~ re78)   # OAL, wAMD-tuned; deterministic
fit          # provenance, retained vs excluded covariates, and the literal next call

m <- oal_match(fit, method = "nearest")        # a genuine matchit object
w <- oal_weight(fit)                           # a genuine weightit object
cobalt::bal.tab(fit)                           # balance with the selected weights

Learn more

• Getting Started with oalasso — what OAL selects and why, the three-line matching and weighting workflows, how the wAMD tuning works, and how to read the output.
• Using oalasso with psAve — appending the OAL score as a candidate in psAve::psave(), when the composition helps, and its caveats.
• Method details and provenance — all formulas, the exact glmnet penalty-scale correction, the provenance of every default, differences from the author’s legacy code, relation to other software, and honest notes on nonlinear extensions.
• Full documentation: https://kabajiro.github.io/oalasso/

Citation

If you use oalasso, please cite the methods it implements, and the package:

Shortreed, S. M., & Ertefaie, A. (2017). Outcome-adaptive lasso: Variable selection for causal inference. Biometrics, 73(4), 1111–1122. doi:[10.1111/biom.12679](https://doi.org/10.1111/biom.12679)

Baldé, I., Yang, Y. A., & Lefebvre, G. (2023). Reader reaction to “Outcome-adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017). Biometrics, 79(1), 514–520. doi:[10.1111/biom.13683](https://doi.org/10.1111/biom.13683) (when method = "goal" is used)

Kabata, D. (2026). oalasso: Outcome-Adaptive Lasso Propensity Scores. R package. https://github.com/kabajiro/oalasso (see citation("oalasso"))

@article{shortreed2017outcome,
  author  = {Shortreed, Susan M. and Ertefaie, Ashkan},
  title   = {Outcome-adaptive lasso: Variable selection for causal inference},
  journal = {Biometrics},
  year    = {2017},
  volume  = {73},
  number  = {4},
  pages   = {1111--1122},
  doi     = {10.1111/biom.12679}
}

@article{balde2023goal,
  author  = {Bald{\'e}, Ismaila and Yang, Yi Archer and Lefebvre, Genevi{\`e}ve},
  title   = {Reader reaction to ``Outcome-adaptive lasso: Variable selection for causal inference'' by Shortreed and Ertefaie (2017)},
  journal = {Biometrics},
  year    = {2023},
  volume  = {79},
  number  = {1},
  pages   = {514--520},
  doi     = {10.1111/biom.13683}
}

License

GPL (>= 2)