Amvik eBook Solutions

One of the primary motivations for clinical trials and observational studies of humans is to infer cause and effect. Disentangling causation from confounding is

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is

This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical

This book is designed to help researchers better design and analyze observational data from quasi-experimental studies and improve the validity of research on c

Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power ana