1 Introducing causality

 

This chapter covers

  • Why and when we need causal inference
  • How causal inference works
  • The difference between observational data and experimental data
  • Reviewing relevant statistical concepts

In many businesses and organizations, when we use machine learning, our goal is to make educated guesses about what will happen in the future. For example, a hospital may want to guess which patients will become very sick soon so doctors can treat those patients first. Often, just being able to make such guesses is enough; understanding why things happen isn’t always necessary.

Causal inference is about figuring out why something happens. More than that, it’s about asking what can be done to change an outcome. For instance, a hospital may want to understand which factors cause a certain illness. If it knows these causes, it can take steps like advising on public health policies or supporting research to develop drugs that prevent the illness, aiming to reduce the number of people who get sick.

1.1 How causal inference works

1.1.1 Step 1: Determine the type of data

1.1.2 Step 2: Understand your problem

1.1.3 Step 3: Create a model

1.1.4 Step 4: Share your model

1.1.5 Step 5: Apply causal inference techniques

1.2 Contrasts between causal models and the predictive models of machine learning

1.3 Experimental studies

1.3.1 Motivating example: Deploying a new website

1.3.2 A/B testing

1.3.3 Randomized controlled trials

1.3.4 Steps to perform an A/B test

1.3.5 Limitations of A/B testing and RCTs

1.4 Observational studies

1.4.1 Simulating synthetic data

1.4.2 Causal effects under confounding

1.5 Reviewing basic statistical concepts

1.5.1 Empirical distributions and data-generating distributions

1.5.2 A refresher on conditional probabilities and expectations

1.6 Further reading

Summary