Causal Inference

( 6 Reviews )

Created by: Michael E. Sobel

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Overall Score : 60 / 100

Course Description

This course offers a rigorous mathematical survey of causal inference at the Master's level.Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use - such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning - to estimate a variety of effects - such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course.

Time: 16h

Instructor Details

Michael E. Sobel

3.0 Rating
6 Reviews

Michael Sobel is a professor of Statistics at Columbia University. His research interests lie primarily in the area of causal inference, where he has published papers on the subjects of mediation, interference, longitudinal causal inference using fixed effects models, meta-analysis, compliance, and causal inference for fMRI experiments, in which massive amounts of time series data are collected for subjects under varying experimental conditions. In addition to extending his work on fMRI, he is also working on interference in observational studies using fixed effects models, and he is working to develop some new estimands for counterfactual inference more broadly.

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Reviews

3.0

6 total reviews

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By Lucas B on 6-Jun-19

A good course. Lot's of insights on Propensity Score Matching. They show good references to those willing to read some articles. Although quick classes, exercises are easy and very practical.

By Charles H on 16-Dec-18

The selection of material is excellent and the professor covers an amazing amount of ground in a handful of lectures. Currently, however, there are many superficial problems with the course, including repeated errors in the quizzes and lectures that are confusing because the slides are missing.

By Max B on 26-Nov-18

Great course. Really interesting and condensed content. A perfect course for analysts and data scientists. I will be recommending this to a few of my colleagues. For some reason there are no slides in week 1 but don't worry there are slides from week 2 onwards

By Byron S on 30-Oct-18

Not having access to slides and materials negates any interest in proceeding with this course.

By Seo-Woo C on 15-May-19

It was difficult to follow lectures without any kind of reading

By Agnes v B on 4-Aug-19

It is a very good intro to CI with proofs and references to recent developments.However, I have to subtract some stars because the quality in material preparation of this course is not up to usual Coursera standards: for the first week there are no slides (so it's hard to follow), and some answers in the exams are not correct. This has been pointed out on this course's discussion forums, but nobody involved in the preparation of this course replies on its discussion forums.