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!
oferecido por
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Universidade da PensilvâniaInformações sobre o curso
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Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 18 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Habilidades que você terá
- Instrumental Variable
- Propensity Score Matching
- Causal Inference
- Causality
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 18 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
Universidade da Pensilvânia
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 - O que você aprenderá com este curso
4 horas para concluir
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 concluir
8 vídeos (Total 128 mín.)
2 horas para concluir
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 concluir
8 vídeos (Total 86 mín.)
5 horas para concluir
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 concluir
12 vídeos (Total 171 mín.)
3 horas para concluir
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 concluir
9 vídeos (Total 119 mín.)
Avaliações
- 5 stars76,85%
- 4 stars18,99%
- 3 stars2,18%
- 2 stars0,87%
- 1 star1,09%
Principais avaliações do A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
por MV14 de nov de 2021
A consise course on causality; watched on 2x speed because the instructor speaks rather slowly; really bad formatting of quiz questions.
por AA15 de mai de 2018
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
por YZ14 de dez de 2021
It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.
por CR10 de fev de 2019
I thought this was a good overview and I'm glad I took the course, but I would have preferred more hands on programming assignments.
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