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!
ofrecido por
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Universidad de PensilvaniaAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
Habilidades que obtendrás
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad de Pensilvania
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.
Programa - Qué aprenderás en este curso
4 horas para completar
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.
4 horas para completar
8 videos (Total 128 minutos)
2 horas para completar
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.
2 horas para completar
8 videos (Total 86 minutos)
5 horas para completar
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.
5 horas para completar
12 videos (Total 171 minutos)
3 horas para completar
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.
3 horas para completar
9 videos (Total 119 minutos)
Reseñas
- 5 stars77,25 %
- 4 stars18,46 %
- 3 stars2,25 %
- 2 stars0,90 %
- 1 star1,12 %
Principales reseñas sobre A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
por JC20 de nov. de 2020
A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.
por WF24 de nov. de 2018
This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.
por OE29 de jul. de 2020
I enjoyed the course a lot and I think I took a lot from it as well. The quizzes and computer projects were appropriate, and the resourcees posted were very useful.
por PD14 de jul. de 2018
Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.
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