A Crash Course in Causality: Inferring Causal Effects from Observational Data
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A Crash Course in Causality: Inferring Causal Effects from Observational Data
University of Pennsylvania
Acerca de este Curso
4.8
92 calificaciones
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!
Habilidades que obtendrás
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
Programa - Qué aprenderás en este curso
Week
1Welcome 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 ejercicios de práctica
Practice Quiz4m
Practice Quiz4m
Causal effects18m
Week
2Confounding 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 ejercicios de práctica
Practice Quiz8m
Identify from DAGs sufficient sets of confounders22m
Week
3Matching 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 ejercicios de práctica
Practice Quiz6m
Practice Quiz8m
Matching12m
Propensity score matching10m
Data analysis project - analyze data in R using propensity score matching16m
Week
4Inverse 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 ejercicios de práctica
Practice Quiz6m
IPTW18m
Data analysis project - carry out an IPTW causal analysis8m
Instructor
Jason A. Roy, Ph.D.
Professor of BiostatisticsDepartment of Biostatistics and Epidemiology
Acerca de University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Preguntas Frecuentes
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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