Causal attribution from retrospective data in Canada's woodland caribou system
Bibliographic record
Abstract
Forecasting the benefits of management interventions intended to improve ecological conditions requires a causal understanding of the factors that lead to system change. The causal attribution of a factor is defined as the difference between the outcome observed in the presence of the factor and the outcome that would have been observed in the factor's absence, that is, the counterfactual condition. Estimating this contrast is relatively straightforward, where matched or randomized controls are available to approximate the counterfactual condition. However, researchers must reason retrospectively from observational data where matched or randomized controls are not available. In this case, the challenge of establishing causal attribution is in estimating the true counterfactual, that is, the outcome that would have resulted from the absence of the factor, given that it was present. Causal analysis permits the estimation of counterfactuals from observational data, assuming that the model captures all common causes between exposure and outcome, that the exposure is independent of other factors in the model (i.e., exogenous), and that the exposure causes the same directional change for all units (i.e., monotonic). I estimated retrospectively the causal attribution of habitat-related factors to recruitment rates in Canada's boreal population of woodland caribou (Rangifer tarandus caribou). Aggregate habitat disturbance had low causal attribution (17.6%). Attribution was greater (29.5%) when habitat disturbance was disaggregated into different factors associated with different pathways of caribou decline. The causal attribution of all habitat factors considered nevertheless rarely exceeded 50%, suggesting that there are other systematic and/or stochastic factors that can limit the effectiveness of current habitat-related recovery actions. More effort is required to understand these factors and how they might be managed to improve the probability of successful caribou recovery.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".