Evaluating Machine Learning Methods for Estimating Heterogeneous Treatment Effects Within a Potential Outcomes Survival Analysis Framework
Bibliographic record
Abstract
Survival analysis is used to predict when an event, such as disease onset, would occur to an individual. Meanwhile, causal inference evaluates whether a treatment changes a targeted outcome while accounting for other factors. This thesis aims to evaluate current ML methodologies designed for causal survival analysis and assess their performance in estimating heterogeneous treatment effects. We conducted simulation studies comparing causal survival forests(CSF), Bayesian additive regression trees(BART), BITES(neural network), and SurvITE(neural network) across ranging levels of confounding and treatment heterogeneity to the theoretical best performance of an oracle model. CSF is the best model in small populations. With higher populations, CSF, BART, and BITES are comparable. All ML models are significantly less precise than the oracle model. This thesis evaluated different ML models in settings simulating randomized and observational studies. Future research should apply these models to real-world datasets and further validate their utility in practical causal survival analysis.
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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 | 0.078 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".