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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Resultados de carreira do aprendiz
33%
comecei uma nova carreira após concluir estes cursos
29%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 18 horas para completar
Inglês
Legendas: Inglês
Habilidades que você terá
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
Resultados de carreira do aprendiz
33%
comecei uma nova carreira após concluir estes cursos
29%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 18 horas para completar
Inglês
Legendas: Inglês
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.)
8 videos
Confusion over causality19min
Potential outcomes and counterfactuals13min
Hypothetical interventions17min
Causal effects19min
Causal assumptions18min
Stratification23min
Incident user and active comparator designs14min
3 exercícios práticos
Practice Quiz30min
Practice Quiz30min
Causal effects30min
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.)
8 videos
Confounding6min
Causal graphs9min
Relationship between DAGs and probability distributions15min
Paths and associations7min
Conditional independence (d-separation)13min
Confounding revisited9min
Backdoor path criterion15min
Disjunctive cause criterion9min
2 exercícios práticos
Practice Quiz30min
Identify from DAGs sufficient sets of confounders30min
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.)
12 videos
Overview of matching12min
Matching directly on confounders13min
Greedy (nearest-neighbor) matching17min
Optimal matching10min
Assessing balance11min
Analyzing data after matching20min
Sensitivity analysis10min
Data example in R16min
Propensity scores11min
Propensity score matching14min
Propensity score matching in R15min
5 exercícios práticos
Practice Quiz30min
Practice Quiz30min
Matching30min
Propensity score matching30min
Data analysis project - analyze data in R using propensity score matching30min
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.)
9 videos
More intuition for IPTW estimation9min
Marginal structural models11min
IPTW estimation11min
Assessing balance9min
Distribution of weights9min
Remedies for large weights13min
Doubly robust estimators15min
Data example in R26min
3 exercícios práticos
Practice Quiz30min
IPTW30min
Data analysis project - carry out an IPTW causal analysis30min
Avaliações
Principais avaliações do 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 WL16 de Mar de 2019
Very easy to follow examples and great coverage for such an important topic! The delivery sometimes get repetitive and I wish we talked more about how the uncertainties are derived.
por MF27 de Dez de 2017
I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.
por FF29 de Nov de 2017
The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.
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