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There are a few other widely used passive causal inference algorithms, but they are left for the final section on | There are a few other widely used passive causal inference algorithms, but they are left for the final section on [[guide:558c318f9a#chap:remaining-topics |Remaining Topics]] Remaining Topics, such as difference-in-difference, regression discontinuity and double machine learning. | ||
==General references== | ==General references== | ||
{{cite arXiv|last1=Cho|first1=Kyunghyun|year=2024|title=A Brief Introduction to Causal Inference in Machine Learning|eprint=2405.08793|class=cs.LG}} | {{cite arXiv|last1=Cho|first1=Kyunghyun|year=2024|title=A Brief Introduction to Causal Inference in Machine Learning|eprint=2405.08793|class=cs.LG}} | ||
Latest revision as of 00:29, 19 May 2024
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In this chapter, we have learned the following concepts:
- Challenges in active causal inference: practical, ethical and legal challenges
- When confounders were observed: Regression, inverse probability weighting and matching
- When confounders were not observed: instrument variables
There are a few other widely used passive causal inference algorithms, but they are left for the final section on Remaining Topics Remaining Topics, such as difference-in-difference, regression discontinuity and double machine learning.
General references
Cho, Kyunghyun (2024). "A Brief Introduction to Causal Inference in Machine Learning". arXiv:2405.08793 [cs.LG].