×

Comment on “Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition”. (English) Zbl 1420.62347

Summary: V. Dorie et al. [ibid. 34, No. 1, 43–68 (2019; Zbl 1420.62345)] (DHSSC) are to be congratulated for initiating the ACIC Data Challenge. Their project engaged the community and accelerated research by providing a level playing field for comparing the performance of a priori specified algorithms. DHSSC identified themes concerning characteristics of the DGP, properties of the estimators, and inference. We discuss these themes in the context of targeted learning.

MSC:

62K20 Response surface designs
68T05 Learning and adaptive systems in artificial intelligence
62B15 Theory of statistical experiments

Citations:

Zbl 1420.62345

Software:

SuperLearner
PDFBibTeX XMLCite
Full Text: DOI Euclid

References:

[1] Benkeser, D. and van der Laan, M. (2016). The highly adaptive lasso estimator. Proc. Int. Conf. Data Sci. Adv. Anal.2016 689-696.
[2] Gruber, S. and van der Laan, M. J. (2010). An application of collaborative targeted maximum likelihood estimation in causal inference and genomics. Int. J. Biostat.6 Art. 18, 31.
[3] Ju, C., Schwab, J. and van der Laan, M. J. (2017). On adaptive propensity score truncation in causal inference. Preprint. Available at arXiv:1707.05861.
[4] Ju, C., Gruber, S., Lendle, S. D., Chambaz, A., Franklin, J. M., Wyss, R., Schneeweiss, S. and van der Laan, M. J. (2017). Scalable collaborative targeted learning for high-dimensional data. Stat. Methods Med. Res.
[5] van der Laan, M. (2017). A generally efficient targeted minimum loss based estimator based on the highly adaptive Lasso. Int. J. Biostat.13 20150097, 35.
[6] van der Laan, M. J. and Gruber, S. (2010). Collaborative double robust targeted maximum likelihood estimation. Int. J. Biostat.6 Art. 17, 70.
[7] van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007). Super learner. Stat. Appl. Genet. Mol. Biol.6 Art. 25, 23. · Zbl 1166.62387 · doi:10.2202/1544-6115.1309
[8] van der Laan, M. J. and Rose, S. (2011). Targeted Learning: Causal Inference for Observational and Experimental Data. Springer Series in Statistics. Springer, New York.
[9] van der Laan, M. J. and Rubin, D. (2006). Targeted maximum likelihood learning. Int. J. Biostat.2 Art. 11, 40.
[10] Zheng, W. and van der Laan, M. J. (2011). Cross-validated targeted minimum-loss-based estimation. In Targeted Learning. Springer Ser. Statist. 459-474. Springer, New York.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.