mlsanityData to 14 Oct 2026

Is More Data All You Need? A Causal Exploration

arXiv 2206.02409 ↗First posted 6 Jun 2022cs.AI cs.CV cs.LGcited by 3

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Abstract (arXiv)

Curating a large scale medical imaging dataset for machine learning applications is both time consuming and expensive. Balancing the workload between model development, data collection and annotations is difficult for machine learning practitioners, especially under time constraints. Causal analysis is often used in medicine and economics to gain insights about the effects of actions and policies. In this paper we explore the effect of dataset interventions on the output of image classification models. Through a causal approach we investigate the effects of the quantity and type of data we need to incorporate in a dataset to achieve better performance for specific subtasks. The main goal of this paper is to highlight the potential of causal analysis as a tool for resource optimization for developing medical imaging ML applications. We explore this concept with a synthetic dataset and an exemplary use-case for Diabetic Retinopathy image analysis.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 35 references that are in the map. First posted on arXiv 6 Jun 2022.

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18 of 35 references.

References · 18

  1. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  2. 2016Learning Representations for Counterfactual Inferencecited by 329
  3. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
  4. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 211
  5. 2019Causality matters in medical imagingcited by 193
  6. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  7. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  8. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
  9. 2019Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Modelscited by 117
  10. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 75
  11. 2018Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learningcited by 56
  12. 2018Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networkscited by 56
6 more
  1. 2015How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?cited by 45
  2. 2020Counterfactual Representation Learning with Balancing Weightscited by 45
  3. 2021Causes of Effects: Learning individual responses from population datacited by 37
  4. 2017Deep Counterfactual Networks with Propensity-Dropoutcited by 30
  5. 2020Causal Inference using Gaussian Processes with Structured Latent Confounderscited by 7
  6. 2018A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenyacited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  2. 2024Causality-Driven Audits of Model Robustnesscited by 2
  3. 2026Causal Transfer in Medical Image Analysiscited by 0