4.7
120 ratings
38 reviews

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 25 hours to complete

Suggested: 5 weeks of study, 3-5 hours per week...

#### English

Subtitles: English

### Skills you will gain

Instrumental VariablePropensity Score MatchingCausal InferenceCausality

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 25 hours to complete

Suggested: 5 weeks of study, 3-5 hours per week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
3 hours to complete

## 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....
8 videos (Total 128 min), 3 quizzes
8 videos
Confusion over causality19m
Potential outcomes and counterfactuals13m
Hypothetical interventions17m
Causal effects19m
Causal assumptions18m
Stratification23m
Incident user and active comparator designs14m
3 practice exercises
Practice Quiz4m
Practice Quiz4m
Causal effects18m
Week
2
2 hours to complete

## 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....
8 videos (Total 86 min), 2 quizzes
8 videos
Causal graphs9m
Relationship between DAGs and probability distributions15m
Paths and associations7m
Conditional independence (d-separation)13m
Confounding revisited9m
Backdoor path criterion15m
Disjunctive cause criterion9m
2 practice exercises
Practice Quiz8m
Identify from DAGs sufficient sets of confounders22m
Week
3
4 hours to complete

## 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....
12 videos (Total 171 min), 5 quizzes
12 videos
Overview of matching12m
Matching directly on confounders13m
Greedy (nearest-neighbor) matching17m
Optimal matching10m
Assessing balance11m
Analyzing data after matching20m
Sensitivity analysis10m
Data example in R16m
Propensity scores11m
Propensity score matching14m
Propensity score matching in R15m
5 practice exercises
Practice Quiz6m
Practice Quiz8m
Matching12m
Propensity score matching10m
Data analysis project - analyze data in R using propensity score matching16m
Week
4
3 hours to complete

## 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....
9 videos (Total 119 min), 3 quizzes
9 videos
More intuition for IPTW estimation9m
Marginal structural models11m
IPTW estimation11m
Assessing balance9m
Distribution of weights9m
Remedies for large weights13m
Doubly robust estimators15m
Data example in R26m
3 practice exercises
Practice Quiz6m
IPTW18m
Data analysis project - carry out an IPTW causal analysis8m
4.7
38 Reviews

## 50%

started a new career after completing these courses

## 18%

got a tangible career benefit from this course

### Top Reviews

By MFDec 28th 2017

I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.

By FFNov 30th 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.

## Instructor

### Jason A. Roy, Ph.D.

Professor of Biostatistics
Department of Biostatistics and Epidemiology