Feature selection in text classification: Identifying spurious words with causal inference methods
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
As has been scrutinized by many, non-causal model may contain spurious correlations that act like shortcuts during the prediction phase, undermining cross-domain accuracy. This can be caused by biased training data that contains spurious words with neutral meanings yet can induce the model to predict wrongly. Based on this assumption, we propose a series of methods to detect these spurious words before feeding the model with the training data. We used advanced causal inference methods which are arising novas in recent studies, such as propensity score matching and inverse propensity score weighting to facilitate the feature selection before training. We experimented with multiple approaches to estimate propensity scores and got profound improvements. We further experimented with BERT model to evaluate the effectiveness of feature selection and find that the model performance with in-domain and out-of-domain testing samples is boosted after we remove the spurious words detected by our methods in the training data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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