Causal Modeling in HR Analytics: A Practical Guide to Models, Pitfalls, and Suggestions
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".