# Business Data Science
> A guide for data-driven decisions

**Author:** Ignacio Martinez
**URL:** https://book.martinez.fyi

## Abstract
This book provides a comprehensive guide to the principles and applications of business data science, with a focus on making sound, data-driven decisions. We begin by laying the groundwork, introducing core concepts such as the potential outcomes framework, the importance of baselines, and the fundamentals of Bayesian thinking. The book then delves into randomized experiments and observational comparisons, covering the design and analysis of RCTs, factorial designs, instrumental variables, and matching as a bridge to observational identification. Next, we explore generalized linear models, from Bayesian linear models to meta-analysis and hurdle models. We then cover stochastic trees and heterogeneous treatment effects, with chapters on Bayesian Additive Regression Trees (BART), Bayesian Causal Forests (BCF), ordinal modeling, and adaptive experimental designs. Finally, we address longitudinal and panel causal inference across time, from aggregate interventions (BSTS and synthetic control) to micro-level panel dynamics with LongBet, connecting dynamic treatment trajectories directly to business decisions. Throughout the book, the emphasis is on the practical application of these methods to solve real-world business problems.