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Causal Inference for Data Science (MEAP V04)

✍ Scribed by Aleix Ruiz de Villa Robert

Publisher
Manning
Year
2023
Tongue
English
Leaves
217
Category
Library
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No coin nor oath required. For personal study only.

✦ Synopsis

This book is for data scientists, but also for machine learning
practitioners/engineers/researchers that may feel the need to include causality in their models. It is also for statisticians and econometricians that want to develop their knowledge on causal inference through machine learning and modeling causality using graphs. Readers may need a basic knowledge of probability (basic distributions, conditional probabilities, ...), statistics (confidence intervals, linear models), machine learning (cross validation and some nonlinear models) and some experience programming.

✦ Table of Contents

MEAP_VERSION_4
Welcome
1_Introduction_to_causality
2_First_steps:_working_with_confounders
3_Applying_causal_inference
4_How_machine_learning_and_causal_inference_can_help_each_other
5_Finding_comparable_cases_with_propensity_scores

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