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Causal Inference

Approx. 12 hours to complete

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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.

Outline

MODULE 1: Key Ideas
Course Overview
Lesson 1: Causation
Lesson 2: Potential Outcome, Unit and Average Effects
Lesson 3: Ignorability: Bridging the Gap Between Randomized Experiments and Observational Studies
Intro Survey
Welcome to Module 1

Module 2: Randomization Inference
Lesson 1: Some Randomized Experiments
Lesson 2: Testing the Null Hypothesis of No Treatment Effect
Lesson 3: Randomization Inference
Welcome to Module 2
Module 2: Assessment

MODULE 3: Regression
Lesson 1: Estimating the Finite Population Average Treatment Effect (FATE) and the Randomized Treatment Effect
Lesson 2: Estimating the ATE: A Regression Approach
Lesson 3: Estimating the ATE: Regression Analysis with Covariates
Welcome to Module 3
Module 3: Assessment

Module 4: Propensity Score
Lesson 1: The Propensity Score
Lesson 2: Estimating the ATE Using Sub-Classification on the Propensity Score
Lesson 3: Estimating the ATE Using Inverse Probability of Treatment Weighting
Welcome to Module 4
Module 4 Assessment

Module 5: Matching
Lesson 1: Matching 1
Lesson 2: More on Matching-Bias and Standard Errors
Welcome to Module 5
Module 5 Assessment

Module 6: Special Topics
Lesson 1: Regression Based Estimators and Double Robustness
Lesson 2: Machine Learning and Estimation of Treatment Effects
Lesson 3: The Unconfoundedness Assumption: Assessment and Sensitivity
Welcome to Module 6
Exit Survey
Module 6: Assessment

Summary of User Reviews

Key Aspect Users Liked About This Course

The course content is thorough and well-structured.

Pros from User Reviews

The instructors are knowledgeable and engaging.
The course is challenging but rewarding.
The assignments and quizzes are helpful and informative.
The course is applicable to real-world scenarios.
The forum discussions are insightful and thought-provoking.

Cons from User Reviews

The course can be time-consuming.
Some of the concepts may be difficult to grasp for beginners.
The course could benefit from more visual aids.
The workload may be overwhelming for some learners.
The course may require prior knowledge in certain areas.

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