UPenn - 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 |
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
- 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
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