Explainability and Causality in Machine Learning through Shapley values
Bibliographic record
Abstract
Explainability and causality are becoming increasingly relevant in Machine Learning research. On the one hand, given the growing use of models in decision-making \nprocesses, the way in which they make predictions needs to be more thoroughly understood. On the other hand, a rising interest exists in formalising and introducing the \ncausal relationships present in the real world into those same models. This work addresses both aspects through the use of Shapley values, a concept that is at the origin \nof SHAP, one of the most popular explainability techniques. Different methods for \ncalculating Shapley values to explain predictions are introduced that take into account \nthe dependence and the causal structure of the data. These methods are illustrated \nand compared through a series of experiments using a database whose causal structure is known. They show that differences can be observed when taking causality into \naccount.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.154 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".