Published on:

14 May 2024

Primary Category:

Machine Learning

Paper Authors:

Kyunghyun Cho

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Probabilistic graphical models as a way to describe data generating processes

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Structural causal models and the do operator

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Regression, randomized controlled trials

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Inverse probability weighting, instrumental variables

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Invariance principle for out-of-distribution generalization

A Simplified Introduction to Causal Inference in Machine Learning

This lecture note provides a simplified introduction to causal inference for machine learning students without prior exposure. It focuses on expanding their view of machine learning to incorporate causal reasoning for better out-of-distribution generalization.

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