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!
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
Universidad de Pensilvania
Acerca de este Curso
51,418 vistas recientes
Resultados profesionales del estudiante
50%
comenzó una nueva carrera después de completar estos cursos
23%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
Habilidades que obtendrás
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
Resultados profesionales del estudiante
50%
comenzó una nueva carrera después de completar estos cursos
23%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 27 horas para completar
Sugerido: 5 weeks of study, 3-5 hours per week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
3 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.
3 horas para completar
8 videos (Total 128 minutos)
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
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)
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
4 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.
4 horas para completar
12 videos (Total 171 minutos)
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
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)
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
Revisiones
Principales revisiones sobre A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
por WLMar 16, 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 MFDec 27, 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 FFNov 29, 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.
por APDec 14, 2018
very good content. Story line is highly concise. However, Lecturer could be more stream-lined the the way of explaining. He sure is a skilled guy, however.
por PDJul 14, 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.
por CEJul 15, 2017
Works best on double speed (from settings menu of each video). Content is delivered in clear and relatable manner using interesting real world examples.
por WFNov 24, 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 AAMay 15, 2018
This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!
por CRFeb 10, 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.
por TLAug 23, 2019
Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.
por MMJan 11, 2020
Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co-workers.
por JIOct 23, 2017
To those with some advanced statistics background, this would truly be helpful to catch up econometric thought processes.
Acerca de 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.
Preguntas Frecuentes
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