Estimating causal effects with machine learning: A guide for ecologists
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
Abstract In ecology, there is a growing need to move beyond correlations to uncovering causal effects from observational data. With the parallel increase in big data and machine learning algorithms, the opportunity now exists to benefit from causal machine learning methodologies. This paper presents an accessible overview of four causal machine learning methods, double machine learning (DML), targeted maximum likelihood estimation (TMLE), deep instrumental variables (Deep IV) and causal forests, that can be applied across ecological contexts. DML and TMLE leverage machine learning to estimate causal effects in the presence of known confounders. Deep IV offers a robust solution for addressing unmeasured confounding or bidirectional relationships by pairing valid instruments with deep neural networks. Causal forests uncover heterogeneity in causal effects, shedding light on context‐dependent ecological responses. Adding these causal machine learning techniques to an ecologist's broader causal toolkit will increase the options researchers have for estimating causal relationships, particularly when dealing with complex and large‐scale observational 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 | 0.002 | 0.001 |
| 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) | 0.000 | 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