A Crash Course in Causality: Inferring Causal Effects from Observational Data (Coursera)

At the end of the course, learners should be able to:

1. Define causal effects using potential outcomes

2. Describe the difference between association and causation

3. Express assumptions with causal graphs

4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)

5. Identify which causal assumptions are necessary for each type of statistical method

So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!

Syllabus

WEEK 1

Welcome and Introduction to Causal Effects

This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.

WEEK 2

Confounding and Directed Acyclic Graphs (DAGs)

This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.

WEEK 3

Matching and Propensity Scores

An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.

WEEK 4

Inverse Probability of Treatment Weighting (IPTW)

Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.

WEEK 5

Instrumental Variables Methods

This module focuses on causal effect estimation using instrumental variables in both randomized trials with non-compliance and in observational studies. The ideas are illustrated with an instrumental variables analysis in R.