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

Instructor: Jason A. Roy, Ph.D.

46,557 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

571 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.

571 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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Shareable certificate

Add to your LinkedIn profile

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 videos Total 128 minutes

Welcome to "A Crash Course in Causality" 1 minute
Confusion over causality 20 minutes
Potential outcomes and counterfactuals 14 minutes
Hypothetical interventions 17 minutes
Causal effects 19 minutes
Causal assumptions 19 minutes
Stratification 24 minutes
Incident user and active comparator designs 15 minutes

3 assignments Total 90 minutes

Practice Quiz 30 minutes
Practice Quiz 30 minutes
Causal effects 30 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 videos Total 86 minutes

Confounding 7 minutes
Causal graphs 9 minutes
Relationship between DAGs and probability distributions 15 minutes
Paths and associations 7 minutes
Conditional independence (d-separation) 13 minutes
Confounding revisited 9 minutes
Backdoor path criterion 16 minutes
Disjunctive cause criterion 10 minutes

2 assignments Total 60 minutes

Practice Quiz 30 minutes
Identify from DAGs sufficient sets of confounders 30 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 videos Total 171 minutes

Observational studies 16 minutes
Overview of matching 13 minutes
Matching directly on confounders 13 minutes
Greedy (nearest-neighbor) matching 17 minutes
Optimal matching 11 minutes
Assessing balance 11 minutes
Analyzing data after matching 20 minutes
Sensitivity analysis 10 minutes
Data example in R 17 minutes
Propensity scores 12 minutes
Propensity score matching 15 minutes
Propensity score matching in R 16 minutes

5 assignments Total 150 minutes

Practice Quiz 30 minutes
Practice Quiz 30 minutes
Matching 30 minutes
Propensity score matching 30 minutes
Data analysis project - analyze data in R using propensity score matching 30 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 videos Total 119 minutes

Intuition for Inverse Probability of Treatment Weighting (IPTW) 12 minutes
More intuition for IPTW estimation 10 minutes
Marginal structural models 12 minutes
IPTW estimation 11 minutes
Assessing balance 10 minutes
Distribution of weights 9 minutes
Remedies for large weights 13 minutes
Doubly robust estimators 16 minutes
Data example in R 27 minutes

3 assignments Total 90 minutes

Practice Quiz 30 minutes
IPTW 30 minutes
Data analysis project - carry out an IPTW causal analysis 30 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 videos Total 125 minutes

Introduction to instrumental variables 11 minutes
Randomized trials with noncompliance 12 minutes
Compliance classes 17 minutes
Assumptions 13 minutes
Causal effect identification and estimation 17 minutes
IVs in observational studies 17 minutes
Two stage least squares 16 minutes
Weak instruments 5 minutes
IV analysis in R 16 minutes

3 assignments Total 90 minutes

Practice Quiz 30 minutes
Practice Quiz 30 minutes
Instrumental variables / Causal effects in randomized trials with non-compliance 30 minutes

Instructor

Instructor ratings

(147 ratings)

Jason A. Roy, Ph.D.

University of Pennsylvania

1 Course 46,557 learners

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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 Dec 14, 2021

It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.

Reviewed on May 22, 2023

Great class！ I have learned a lot on causal inference to conduct experiment analysis at work. The R coding sessions and lectures on the logic/math behind are really helpful.

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