mlsanityData to 14 Oct 2026

A Brief Introduction to Causal Inference in Machine Learning

arXiv 2405.08793 ↗First posted 14 May 2024cs.LGcited by 1

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Abstract (arXiv)

This is a lecture note produced for DS-GA 3001.003 "Special Topics in DS - Causal Inference in Machine Learning" at the Center for Data Science, New York University in Spring, 2024. This course was created to target master's and PhD level students with basic background in machine learning but who were not exposed to causal inference or causal reasoning in general previously. In particular, this course focuses on introducing such students to expand their view and knowledge of machine learning to incorporate causal reasoning, as this aspect is at the core of so-called out-of-distribution generalization (or lack thereof.)

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 14 May 2024.

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6 of 25 references.

References · 6

  1. 2013Auto-Encoding Variational Bayescited by 4,053
  2. 2019Invariant Risk Minimizationcited by 1,854
  3. 2019Neural Text Generation with Unlikelihood Trainingcited by 503
  4. 2019Unlearn Dataset Bias in Natural Language Inference by Fitting the Residualcited by 119
  5. 2022Causal Machine Learning: A Survey and Open Problemscited by 110
  6. 2023Active and Passive Causal Inference Learningcited by 1

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Citing papers per year: 2024: 1.

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  1. 2024FairPFN: Transformers Can do Counterfactual Fairnesscited by 8

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