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!
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A Crash Course in Causality: Inferring Causal Effects from Observational Data
Université de PennsylvanieÀ propos de ce cours
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Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 18 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Compétences que vous acquerrez
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Niveau intermédiaire
Approx. 18 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol
Offert par
Université de Pennsylvanie
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.
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
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 heures pour terminer
8 vidéos (Total 128 min)
2 heures pour terminer
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 heures pour terminer
8 vidéos (Total 86 min)
5 heures pour terminer
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 heures pour terminer
12 vidéos (Total 171 min)
3 heures pour terminer
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 heures pour terminer
9 vidéos (Total 119 min)
Avis
Meilleurs avis pour A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
par WJ11 sept. 2021
Great introduction on the causal analysis.The instructor did a great job on explaining the topic in a logical and rigorous way. R codes are very relevant and helpful to digest the material as well.
par JC20 nov. 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.
par WL16 mars 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.
par MF27 déc. 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.
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