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Causal Inference
  • Level Expert
  • Duration 13 hours
  • Course by Columbia University
  • Offered by

About

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.

Modules

Course Overview

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1 Videos

  • Course Overview

Intro Survey

    • 1 Readings
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1 Readings

  • Intro Survey

Welcome to Module 1

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1 Readings

  • Welcome to Module 1

Module 1, Lesson 1: Causation

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1 Videos

  • Lesson 1: Causation

Module 1, Lesson 2: Potential Outcome, Unit and Average Effects

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1 Videos

  • Lesson 2: Potential Outcome, Unit and Average Effects

Module 1, Lesson 3: Ignorability: Bridging the Gap Between Randomized Experiments and Observational Studies

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1 Videos

  • Lesson 3: Ignorability: Bridging the Gap Between Randomized Experiments and Observational Studies

Welcome to Module 2

    • 1 Readings
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1 Readings

  • Welcome to Module 2

Module 2, Lesson 1: Some Randomized Experiments

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  • Lesson 1: Some Randomized Experiments

Module 2, Lesson 2: Testing the Null Hypothesis of No Treatment Effect

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1 Videos

  • Lesson 2: Testing the Null Hypothesis of No Treatment Effect

Module 2, Lesson 3: Randomization Inference

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1 Videos

  • Lesson 3: Randomization Inference

Module 2: Assessment

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1 Assignment

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Welcome to Module 3

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  • Welcome to Module 3

Module 3, Lesson 1: Estimating the Finite Population Average Treatment Effect (FATE) and the Randomized Treatment Effect

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1 Videos

  • Lesson 1: Estimating the Finite Population Average Treatment Effect (FATE) and the Randomized Treatment Effect

Module 3, Lesson 2: Estimating the ATE: A Regression Approach

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  • Lesson 2: Estimating the ATE: A Regression Approach

Module 3, Lesson 3: Estimating the ATE: Regression Analysis with Covariates

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  • Lesson 3: Estimating the ATE: Regression Analysis with Covariates

Module 3: Assessment

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Welcome to Module 4

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1 Readings

  • Welcome to Module 4

Module 4, Lesson 1: The Propensity Score

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1 Videos

  • Lesson 1: The Propensity Score

Module 4, Lesson 2: Estimating the ATE Using Sub-Classification on the Propensity Score

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1 Videos

  • Lesson 2: Estimating the ATE Using Sub-Classification on the Propensity Score

Module 4, Lesson 3: Estimating the ATE Using Inverse Probability of Treatment Weighting

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1 Videos

  • Lesson 3: Estimating the ATE Using Inverse Probability of Treatment Weighting

Module 4: Assessment

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Welcome to Module 5

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Module 5, Lesson 1: Matching 1

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  • Lesson 1: Matching 1

Module 5, Lesson 2: More on Matching-Bias and Standard Errors

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  • Lesson 2: More on Matching-Bias and Standard Errors

Module 5: Assessment

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Welcome to Module 6

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  • Welcome to Module 6

Module 6, Lesson 1: Regression Based Estimators and Double Robustness

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  • Lesson 1: Regression Based Estimators and Double Robustness

Module 6, Lesson 2: Machine Learning and Estimation of Treatment Effects

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  • Lesson 2: Machine Learning and Estimation of Treatment Effects

Module 6, Lesson 3: The Unconfoundedness Assumption: Assessment and Sensitivity

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  • Lesson 3: The Unconfoundedness Assumption: Assessment and Sensitivity

Module 6: Assessment

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1 Assignment

  • Module 6: Assessment

Exit Survey

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1 Readings

  • Exit Survey

Auto Summary

"Causal Inference" is an expert-level course in Maths & Statistics offered by Coursera. It rigorously explores causal inference methods, crucial in science, medicine, policy, and business. Over 780 minutes, learners will delve into statistical literature, study data collection methods, and evaluate techniques like matching and machine learning. Ideal for those seeking advanced understanding of causal relationships, this course offers a comprehensive introduction to this transformative field. Subscription option: Starter.

Michael E. Sobel
Instructor

Michael E. Sobel