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

Instructor: Jason A. Roy, Ph.D.

46,891 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

573 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

90%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

573 reviews

Intermediate level

Some related experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

90%

Most learners liked this course

Skills you'll gain

Tools you'll learn

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Assessments

16 assignments

Taught in English

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There are 5 modules in this course

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!

Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). 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!

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.

What's included

8 videos3 assignments

8 videosTotal 128 minutes

Welcome to "A Crash Course in Causality"1 minute
Confusion over causality20 minutes
Potential outcomes and counterfactuals14 minutes
Hypothetical interventions17 minutes
Causal effects19 minutes
Causal assumptions19 minutes
Stratification24 minutes
Incident user and active comparator designs15 minutes

3 assignmentsTotal 90 minutes

Practice Quiz30 minutes
Practice Quiz30 minutes
Causal effects30 minutes

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.

What's included

8 videos2 assignments

8 videosTotal 86 minutes

Confounding7 minutes
Causal graphs9 minutes
Relationship between DAGs and probability distributions15 minutes
Paths and associations7 minutes
Conditional independence (d-separation)13 minutes
Confounding revisited9 minutes
Backdoor path criterion16 minutes
Disjunctive cause criterion10 minutes

2 assignmentsTotal 60 minutes

Practice Quiz30 minutes
Identify from DAGs sufficient sets of confounders30 minutes

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.

What's included

12 videos5 assignments

12 videosTotal 171 minutes

Observational studies16 minutes
Overview of matching13 minutes
Matching directly on confounders13 minutes
Greedy (nearest-neighbor) matching17 minutes
Optimal matching11 minutes
Assessing balance11 minutes
Analyzing data after matching20 minutes
Sensitivity analysis10 minutes
Data example in R17 minutes
Propensity scores12 minutes
Propensity score matching15 minutes
Propensity score matching in R16 minutes

5 assignmentsTotal 150 minutes

Practice Quiz30 minutes
Practice Quiz30 minutes
Matching30 minutes
Propensity score matching30 minutes
Data analysis project - analyze data in R using propensity score matching30 minutes

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.

What's included

9 videos3 assignments

9 videosTotal 119 minutes

Intuition for Inverse Probability of Treatment Weighting (IPTW)12 minutes
More intuition for IPTW estimation10 minutes
Marginal structural models12 minutes
IPTW estimation11 minutes
Assessing balance10 minutes
Distribution of weights9 minutes
Remedies for large weights13 minutes
Doubly robust estimators16 minutes
Data example in R27 minutes

3 assignmentsTotal 90 minutes

Practice Quiz30 minutes
IPTW30 minutes
Data analysis project - carry out an IPTW causal analysis30 minutes

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.

What's included

9 videos3 assignments

9 videosTotal 125 minutes

Introduction to instrumental variables11 minutes
Randomized trials with noncompliance12 minutes
Compliance classes17 minutes
Assumptions13 minutes
Causal effect identification and estimation17 minutes
IVs in observational studies17 minutes
Two stage least squares16 minutes
Weak instruments5 minutes
IV analysis in R16 minutes

3 assignmentsTotal 90 minutes

Practice Quiz30 minutes
Practice Quiz30 minutes
Instrumental variables / Causal effects in randomized trials with non-compliance30 minutes

Instructor

Instructor ratings

(148 ratings)

Jason A. Roy, Ph.D.

University of Pennsylvania

1 Course46,891 learners

Offered by

University of Pennsylvania

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Reviewed on Mar 11, 2021

Excellent video lectures. Challenging end of module quizzes. I found more challenging doing the practical exercises because I had no experience with R.

Reviewed on Nov 20, 2020

A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.

Reviewed on Apr 4, 2021

My work involves working with observational data. This course taught me to think in more formal and organized way on topics and questions of causal inference.

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