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Comentários e feedback de alunos de A Crash Course in Causality: Inferring Causal Effects from Observational Data da instituição Universidade da Pensilvânia

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Sobre o curso

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! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!...

Melhores avaliações

WJ

11 de set de 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.

MF

27 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.

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76 — 100 de 148 Avaliações para o A Crash Course in Causality: Inferring Causal Effects from Observational Data

por CAIWEI Z

4 de ago de 2019

This course is very suitable for beginners, clear and easy to understand.

por Vikram R

14 de mar de 2018

Great course for getting your hands dirty with some real causal methods.

por olufemi B o

22 de ago de 2019

The course itremendoulsy straightened my knowledge of causal evaluation

por Bob K

16 de out de 2018

Well taught, easy to follow but potentially very important techniques

por Gautam B

17 de fev de 2020

Great intro and overview of the details of Causal Inference methods

por Rudy M P

17 de abr de 2018

I learned the basics of causality inference and want even more now!

por Alessandro C

31 de mar de 2020

Very clear, it give good intuition also for technical points.

por keyvan R

1 de set de 2020

great course and practical introduction to causal inference.

por Ziyang H

27 de jul de 2020

A good course with detailed explanation and data examples

por Mohammed S

4 de set de 2020

Excellent course in causal effect estimation. Thanks .

por Aniket G

15 de dez de 2019

Superb crash course for quickly getting up to speed!

por Zhe C

21 de abr de 2022

I learned a lot from this course! Highly recommend!

por Marriane M

8 de out de 2019

Very practical for beginners in causal inference

por Min-hyung K

1 de jul de 2017

Thanks so much for providing this great lecture.

por Arka B

31 de mai de 2018

gives thorough basic intro to causal inference

por Michael S

7 de jul de 2019

Awesome!!! Looking forward to the next one!!!

por Tarashankar B

8 de set de 2020

Detailed and excellent course on causality

por Pichaya T

26 de fev de 2018

Excellent courses. I gain my expectations.

por Akin A C

3 de jan de 2021

excellent course, very very useful!!

por Takahiro I

26 de set de 2017

The best lecture series of causality

por Clancy B

28 de ago de 2018

no nonsense, in depth and practical

por Carolina S

18 de mai de 2021

A very good introduction course.

por Paulo Y C

2 de ago de 2020

intense and well crafted course!

por William L

3 de abr de 2020

wonderful course, very helpful

por Bob H

19 de out de 2017

Good intro of the techniques.