# Causal Inference

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.

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Rating 2.8★ based on 8 ratings 7 weeks 6 weeks of study, 3-5 hours per week Jul 3 (23 weeks ago) \$49 Columbia University via Coursera Michael E. Sobel On all desktop and mobile devices English Data Science Mathematics Data Science Math And Logic Probability And Statistics

## What people are saying

covers an amazing amount

The selection of material is excellent and the professor covers an amazing amount of ground in a handful of lectures.

talking head throwing notation

It was difficult to follow lectures without any kind of reading The first week is a throw-away, as there are no slides, just a talking head throwing notation at you.

materials negates any interest

things could be explained

The teacher is great, but some things could be explained more clearly.

insights on propensity score

Lot's of insights on Propensity Score Matching.

although quick classes

Although quick classes, exercises are easy and very practical.

explained more clearly

many superficial problems

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.

usual coursera standards

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.

it was difficult

really interesting

Really interesting and condensed content.

score matching

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