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

Instructor: Jason A. Roy, Ph.D.

Access provided by New York State Department of Labor

44,731 already enrolled

5 modules

Gain insight into a topic and learn the fundamentals.

4.7

(564 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.

4.7

(564 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

Details to know

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 鈥渃orrelation does not equal causation.鈥� 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 minutePreview module
Confusion over causality19 minutes
Potential outcomes and counterfactuals13 minutes
Hypothetical interventions17 minutes
Causal effects19 minutes
Causal assumptions18 minutes
Stratification23 minutes
Incident user and active comparator designs14 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

Confounding6 minutesPreview module
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 criterion15 minutes
Disjunctive cause criterion9 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 studies15 minutesPreview module
Overview of matching12 minutes
Matching directly on confounders13 minutes
Greedy (nearest-neighbor) matching17 minutes
Optimal matching10 minutes
Assessing balance11 minutes
Analyzing data after matching20 minutes
Sensitivity analysis10 minutes
Data example in R16 minutes
Propensity scores11 minutes
Propensity score matching14 minutes
Propensity score matching in R15 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)11 minutesPreview module
More intuition for IPTW estimation9 minutes
Marginal structural models11 minutes
IPTW estimation11 minutes
Assessing balance9 minutes
Distribution of weights9 minutes
Remedies for large weights13 minutes
Doubly robust estimators15 minutes
Data example in R26 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 124 minutes

Introduction to instrumental variables11 minutesPreview module
Randomized trials with noncompliance11 minutes
Compliance classes16 minutes
Assumptions13 minutes
Causal effect identification and estimation16 minutes
IVs in observational studies17 minutes
Two stage least squares15 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

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Instructor

Instructor ratings

4.7 (146 ratings)

Jason A. Roy, Ph.D.

University of Pennsylvania

1 Course44,731 learners

Offered by

University of Pennsylvania

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4.7

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Reviewed on Apr 5, 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.

Reviewed on Mar 12, 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 30, 2017

The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.

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