# University of Pennsylvania - A Crash Course in Causality: Inferring Causal Effects from Observational Data

• Offered byCoursera

## A Crash Course in Causality: Inferring Causal Effects from Observational Data at Coursera Overview

 Duration 18 hours Start from Start Now Total fee Free Mode of learning Online Difficulty level Intermediate Official Website Explore Free Course Credential Certificate

## A Crash Course in Causality: Inferring Causal Effects from Observational Data at Coursera Highlights

• 33% started a new career after completing these courses.
• 27% got a tangible career benefit from this course.
• Earn a shareable certificate upon completion.

## A Crash Course in Causality: Inferring Causal Effects from Observational Data at Coursera Course details

Skills you will learn
• 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!

## A Crash Course in Causality: Inferring Causal Effects from Observational Data at Coursera Curriculum

Welcome and Introduction to Causal Effects

Welcome to "A Crash Course in Causality"

Confusion over causality

Potential outcomes and counterfactuals

Hypothetical interventions

Causal effects

Causal assumptions

Stratification

Incident user and active comparator designs

Practice Quiz

Practice Quiz

Causal effects

Confounding and Directed Acyclic Graphs (DAGs)

Confounding

Causal graphs

Relationship between DAGs and probability distributions

Paths and associations

Conditional independence (d-separation)

Confounding revisited

Backdoor path criterion

Disjunctive cause criterion

Practice Quiz

Identify from DAGs sufficient sets of confounders

Matching and Propensity Scores

Observational studies

Overview of matching

Matching directly on confounders

Greedy (nearest-neighbor) matching

Optimal matching

Assessing balance

Analyzing data after matching

Sensitivity analysis

Data example in R

Propensity scores

Propensity score matching

Propensity score matching in R

Practice Quiz

Practice Quiz

Matching

Propensity score matching

Data analysis project - analyze data in R using propensity score matching

Inverse Probability of Treatment Weighting (IPTW)

Intuition for Inverse Probability of Treatment Weighting (IPTW)

More intuition for IPTW estimation

Marginal structural models

IPTW estimation

Assessing balance

Distribution of weights

Remedies for large weights

Doubly robust estimators

Data example in R

Practice Quiz

IPTW

Data analysis project - carry out an IPTW causal analysis

Instrumental Variables Methods

Introduction to instrumental variables

Randomized trials with noncompliance

Compliance classes

Assumptions

Causal effect identification and estimation

IVs in observational studies

Two stage least squares

Weak instruments

IV analysis in R

Practice Quiz

Practice Quiz

Instrumental variables / Causal effects in randomized trials with non-compliance

## Important Dates

May 25, 2024
Course Commencement Date

## Other courses offered by Coursera

– / –
Start Now
– / –
– / –
Free
– / –
Free
Start Now
View Other 6713 Courses

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

### Student Forum

Anything you would want to ask experts?
Write here...