CATE meets ML - The Conditional Average Treatment Effect and Machine Learning

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CATE meets ML - The Conditional Average Treatment Effect and Machine Learning

This course deals with the intersection of causal inference, especially heterogeneity, and Machine Learning. We will see why and how Machine Learning can be useful when estimating treatment effects.

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  • 13 Students Enrolled
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Tags:
CATE MachineLearning



Courselet Content

1 courselets • 2 courselet components • 00h 30m total length

Requirements

  • Knowledge of Econometrics and Machine Learning.

Description

This course deals with the intersection of causal inference, especially heterogeneity, and Machine Learning.
We will see why and how Machine Learning can be useful when estimating treatment effects.

This is an introductory course that deals with methods that estimate the CATE. An empirical example, evaluating the effects of microcredits, sets the methods into perspective.

 

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Last Updated 3rd June 2022
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