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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.
In Causal Inference for Data Science you will learn how to
Model reality using causal graphs
Estimate causal effects using statistical and machine learning techniques
Determine when to use A/B tests, causal inference, and machine learning
Explain and assess objectives, assumptions, risks, and limitations
Determine if you have enough variables for your analysis
It’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You’ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions.
About the technology
A/B tests or controlled trials are expensive and often unfeasible in a business environment. Causal inference is a powerful methodology that allows a data scientist to identify causes from data, even when no experiment or test has been performed. Using causal methods increases the level of confidence in business decision making by clearly connecting causes and effects.
CAUSAL INFERENCE AND THE ROLE OF CONFOUNDERS
Introduction to causality
First steps: working with confounders
Applying causal inference
How machine learning and causal inference can help each other
TOOLS FOR MITIGATING THE EFFECTS OF CONFOUNDERS
Finding comparable cases with propensity scores
Direct and indirect effects with linear models
Dealing with complex graphs
Advanced tools for variable adjustment
ADDITIONAL TOOLS FOR CAUSAL INFERENCE
Potential outcomes framework
Instrumental variables
The impact of a meaningful event