Reflections on Learning from Observational Data
Bibliographic record
Abstract
The social sciences study various aspects of human behaviour – social, economic and political – based on observational data. Observational data are inaccurate and subject to simultaneity, seasonality, structural breaks, random variation and too many interlocking variables masking the underlying causal patterns. During the past two decades or so, the use experimental data (RCTs) has become widely popular across the social sciences, creating a tension between the supporters and critics of the new and the old methodologies. In this paper, we first review these methodologies, both observational and experimental, focusing on how economists and other social scientists try to learn about the underlying causal relationships from the correlations contained in the data. We then reflect on whether the new or the old methodologies should be the way forward from a purely statistical and a broader policy and development perspectives.
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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 | 0.238 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.080 |
| Scholarly communication | 0.017 | 0.068 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.020 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".