Causal Modeling in HR Analytics: A Practical Guide to Models, Pitfalls, and Suggestions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims at introducing the concept, importance, and technique of causal modeling in HR Analytics. Based on a systematic review of the current peer- review articles on HR Analytics published in scholarly journals that are included in Journal Quality List (2016), we concluded that the criticism of HR Analytics as “a management fad” or “fail(ing) the big data challenge” is mostly true. Our analysis shows that the main reason that researchers hold a pessimistic view of the tool is due to a lack of causal reasoning in statistical modeling. The purposes of this paper are three-fold: 1) We explain different purposes of statistical modeling used in HR Analytics. 2) We describe what causal modeling is and why it is critical for the HR practitioners to adopt such technique in HR decision- making process. 3) We provide a list of techniques to ensure causality and enlighten decision-making based on scholarly literature on methods. 4) We discussed what variables should be considered as the consequences of such modeling techniques in HR context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it