Causal Inference For Data Science

When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning.A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. Causal Inference for Data Science reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed.In Causal Inference for Data Science you will learn how to:Model reality using causal graphsEstimate causal effects using statistical and machine learning techniquesDetermine when to use A/B tests, causal inference, and machine learningExplain and assess objectives, assumptions, risks, and limitationsDetermine if you have enough variables for your analysisIt’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene...