Causal Inference for Data Science (MEAP V04)

Description

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. 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