Estimating causal effects with machine learning: A guide for ecologists
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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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".