Bibliographic record
Abstract
<p>This thesis studies how the field of causality can mathematically define the causal relationships between events distinguishing causal effects from statistically observed correlation. We concentrate on basic causality concepts and definitions of tools and we ask which aspects of the theory can be used to approach practical problems in a systematic manner. This work demonstrates basic causality methods such as building causal models, working with them using d-separation, do-calculus, and methods associated with identification of causal relationships and resolving interventional and contrafactual queries. Necessary assumptions that we need to make before we start building and working with causal models will be outlined. We will demonstrate the theory using simple examples, on the domain of categorical and numerical data. We also will present a simple diagnostic example which aims to fold overviewed tools into a practical application. For all examples data was generated synthetically for the absence of reliable publicly available ground truth data designed for causality. We conclude the thesis by outlining our experience studying and working with the theory, what value we see in using it, as well laying out the benefits and challenges of the theory.</p>
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.
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 | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 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".