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Automated analyses: Because we can, does it mean we should?
| Content Provider | Semantic Scholar |
|---|---|
| Author | Shortreed, Susan M. Moodie, Erica E. M. |
| Copyright Year | 2019 |
| Abstract | We commend Benkeser, Cai and van der Laan (2019) for their interesting proposal and efforts to further automate the machinery of collaborative targeted minimum loss estimation (TMLE). Reducing human impact on an analysis, i.e. to circumvent the need for analysts to “select an increasingly complex sequence of estimators [...] and implement each of these” is an important goal that could bring us closer to reproducible and transparent research. We agree that striving for estimators which have stable properties is a benefit, and practical positivity violations can render many estimators “erratic” or “non-robust”. In the examples, the authors showcase success in constructing data-specific robust estimators with well-behaved properties. Petersen et al. (2010) describe TMLE as “an explicit trade-off [that is ideally] made in a systematic way rather than on an ad hoc basis at the discretion of the investigator.” No statistical or machine learning-based approach is exempt from human-made decisions. For example, in ensemble-based machine learning methods, often used in conjunction with TMLE, the analyst must choose which methods to include in the ensemble learner, select hyperparameter values (e.g. random forests minimum node size), and select the number of folds for cross-validation. The question then arises as to whether it does, or should, trouble the scientific community that TMLE is less automated than we might think. Here we wish to probe two fundamental questions: Should automation and data-driven analyses be preferred when inferential, rather than predictive, analyses are undertaken? For example, is a data-adaptive estimand or an a priori human-defined estimand preferred? What do we lose by automating an increasing number of steps of scientific discovery? |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.e-publications.org/ims/submission/STS/user/submissionFile/43484?confirm=4edb114c |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
