What's Missing from Machine Learning for Medicine? New Methods for Causal Effect Estimation and Representation Learning from EHR Data
Bibliographic record
Abstract
This thesis explores the applications of deep learning in clinical and epidemiologic data analysis, focusing on neural networks for causal effect estimation and clinical risk prediction. I claim that neural networks have a significant role in the future of causal inference and I present empirical results in Chapter 2 that demonstrate the superiority of neural nets for solving the fundamental integral equation of proximal inference. In Chapters 3 and 4, I discuss the limitations of deep learning in the context of clinical risk prediction. Chapter 3 analyzes the field's progress on modeling structured medical data using deep learning and shows that it has been stagnant. I propose several reasons for this stagnation and attempt to address some of them with a novel Transformer architecture in Chapter 4. This pre-trained Transformer model, called Labrador, does not consistently outperform tree-based methods in downstream fine-tuning tasks despite showing strong results on pre-training. This observation motivates several concluding arguments that I present in the final chapter. Among them, I emphasize that the full potential of deep learning in medical artificial intelligence is yet to be realized. In order to realize some of this potential, I argue that multi-modal models are required and that coordinated institutional efforts will be necessary to foster the resources for large-scale data and model training. Finally, I discuss some of the remarkable capabilities recently observed in GPT-4 and hypothesize that large language models, despite being purely predictive in nature, can learn about causality by training on sufficiently large data. I claim that this emergent understanding of causality and acquisition of world models should be anticipated as an outcome of next-token prediction. I use the human cortex as an existence proof for this statement and draw a connection between next-token prediction methods and the predictive coding theory of neuroscience for information processing in the cortex. In conclusion, I discuss the implications of large language models for the practice of causal inference and vice versa.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".