On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics | Paper Citation Record

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This paper cites Prediction-Powered Inference.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Prediction-Powered Inference

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Invariant Risk Minimization

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Layer Normalization

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Bang and J

Reference 4

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Biopharmaceutical industry-sponsored clinical trials: Impact on state economies, 2015

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Probing classifiers: Promises, shortcomings, and advances

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Random forests

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This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Sparse Autoencoders Find Highly Interpretable Features in Language Models

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This paper cites Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

Reference 9

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Causal scrubbing: a method for rigorously testing interpretability hypotheses

Reference 10

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Unresolved cited work

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Unresolved cited work

Reference 12

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Transcoders find interpretable llm feature circuits

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics A mathematical framework for transformer circuits

Reference 14

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Data on notable ai models, 2024

Reference 15

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This paper cites Modern robust statistical methods: an easy way to maximize the accuracy and power of your research.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Modern robust statistical methods: an easy way to maximize the accuracy and power of your research

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This paper cites Balance Regularized Neural Network Models for Causal Effect Estimation.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Balance Regularized Neural Network Models for Causal Effect Estimation

Reference 17

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Schapire

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Friedman

Reference 19

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Scaling and evaluating sparse autoencoders

Reference 20

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Causal abstractions of neural networks

Reference 21

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Inducing causal structure for interpretable neural networks

Reference 22

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Deep Learning

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Natural and Political Observations Mentioned in a Following Index, and Made Upon the Bills of Mortality

Reference 24

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Deep Residual Learning for Image Recognition

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Burgess, Xavier Glorot, Matthew M

Reference 26

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Denoising diffusion probabilistic models

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Long short-term memory

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics An artificial neural network for spatio-temporal bipolar patterns: application to phoneme classification

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Multilayer feedforward networks are universal approximators

Reference 30

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Maximum mean discrepancy for class ratio estimation

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Johansson, Uri Shalit, and David Sontag

Reference 32

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Highly accurate protein structure prediction with AlphaFold

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Linear Representations of Political Perspective Emerge in Large Language Models

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Auto-Encoding Variational Bayes

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Using multiple imputation to deal with missing data and attrition in longitudinal studies with patient-reported outcomes

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Auditing language models for hidden objectives, 2025

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Locating and Editing Factual Associations in GPT

Reference 38

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Meyes, M

Reference 39

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Perceptrons: An Introduction to Computational Geometry

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Attribution patching: Activation patching at industrial scale

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Niven and H.-Y

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Sparse Autoencoders Trained on the Same Data Learn Different Features

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Causality: Models, Reasoning, and Inference

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Prinja, N

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Stochastic backpropagation and approximate inference in deep generative models

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics The perceptron: A probabilistic model for information storage and organization in the brain

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Polysemanticity and Capacity in Neural Networks

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Toward causal representation learning

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Adjustment for confounding using pre-trained representations

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics 271 the neyman— rubin model of causal inference and estimation via matching methods

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Johansson, and David Sontag

Reference 54

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Shi, Victor Veitch, and David M

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On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Blei, and Victor Veitch

Reference 56

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This paper cites Blei, and Victor Veitch.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Blei, and Victor Veitch

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.790776Z

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Observation 64ca9f31-dbf6-4006-b9c5-f8a8909a2177 · outbound

This paper cites Longitudinal targeted minimum loss-based estimation with temporal-difference heterogeneous transformer.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Longitudinal targeted minimum loss-based estimation with temporal-difference heterogeneous transformer

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.745219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0641359-7bc8-44e8-93f1-c76740c0f533 · outbound

This paper cites To explain or to predict? Statistical Science, 25 0 (3): 0 289--310, 2010.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics To explain or to predict? Statistical Science, 25 0 (3): 0 289--310, 2010

Reference 59

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This paper cites Regression shrinkage and selection via the Lasso.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Regression shrinkage and selection via the Lasso

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.623035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc2e859d-3451-47d7-a64d-90ddba7bfd55 · outbound

This paper cites van der Laan and James M.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan and James M

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.611712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2d7a00e-8481-4c0d-a706-764b420d883e · outbound

This paper cites van der Laan and Sherri Rose.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan and Sherri Rose

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.602090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f54a839-77a8-417d-890f-2fe00e62ede4 · outbound

This paper cites van der Laan and Daniel Rubin.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan and Daniel Rubin

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.591780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 03f98dff-1e31-46f2-8ea5-5bc8200fcc22 · outbound

This paper cites van der Laan, Eric C.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan, Eric C

Reference 65

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Source-reported events for the cited work

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Observation f2a9e5a8-02e7-4d38-9da5-e512fbe695c7 · outbound

This paper cites van der Laan, Maya L.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan, Maya L

Reference 66

Resolution: verified exact; doi, observed 2026-08-16T04:43:33.898596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c07965f4-5356-4c6b-bee1-911bae521ce8 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.583060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1f42733f-1f12-4e77-977a-7661403ccba8 · outbound

This paper cites Deep learning-based propensity scores for confounding control in comparative effectiveness research: A large-scale, real-world data study.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Deep learning-based propensity scores for confounding control in comparative effectiveness research: A large-scale, real-world data study

Reference 69

Resolution: verified exact; doi, observed 2026-08-16T04:43:33.697522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1429f68d-0e3c-4c6b-b635-ac459b7e3f4d · outbound

This paper cites Causal Proxy Models for Concept-Based Model Explanations.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Causal Proxy Models for Concept-Based Model Explanations

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:43:34.006477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0a0d6e7-ae3b-44f3-a425-5b8018a87c45 · outbound

This paper cites van der Laan.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics van der Laan

Reference 71

Resolution: verified exact; doi, observed 2026-08-16T04:43:33.964004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d20039a-72af-4385-80ce-5c13c494e1d5 · outbound

This paper cites Ensemble Methods: Foundations and Algorithms.

On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statistics Ensemble Methods: Foundations and Algorithms

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:43:34.573053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Pith citing papers