Causal Inference 2 (Coursera)

Offered by Columbia University,
Causal Inference 2 (Coursera)

This course offers a rigorous mathematical survey of advanced topics in 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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

We will study advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models.

Syllabus

WEEK 1: Introduction to Mediation
WEEK 2: More on Mediation
WEEK 3: Instrumental Variables, Principal Stratification, and Regression Discontinuity
WEEK 4: Longitudinal Causal Inference
WEEK 5: Interference and Fixed Effects

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Exploration et production de données pour les entreprises (Coursera) Coursera
University of Illinois at Urbana-Champaign

Exploration et production de données pour les entreprises (Coursera)

Ce cours fournit un cadre analytique afin de vous aider à évaluer les problèmes clés de manière structurée. Il vous procurera également des outils afin de mieux gérer les incertitudes qui envahissent et compliquent les processus des entreprises. Plus précisément, vous serez initié(e) aux statistiques et à la manière de résumer les données. Vous découvrirez les concepts de fréquence, de loi normale, d’études statistiques, de l’échantillonnage et des intervalles de confiance.

Sep 14th 2026
4 Weeks
Inferential Statistical Analysis with Python (Coursera) Coursera
University of Michigan

Inferential Statistical Analysis with Python (Coursera)

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.

Sep 7th 2026
4 Weeks
Combinatorics and Probability (Coursera) Coursera
University of California, San Diego,Higher School of Economics - HSE University

Combinatorics and Probability (Coursera)

Counting is one of the basic mathematically related tasks we encounter on a day to day basis. The main question here is the following. If we need to count something, can we do anything better than just counting all objects one by one? Do we need to create a list of all phone numbers to ensure that there are enough phone numbers for everyone? Is there a way to tell that our algorithm will run in a reasonable time before implementing and actually running it? All these questions are addressed by a mathematical field called Combinatorics.

Sep 7th 2026
5-12 Weeks
Think Again III: How to Reason Inductively (Coursera) Coursera
Duke University

Think Again III: How to Reason Inductively (Coursera)

Want to solve a murder mystery? What caused your computer to fail? Who can you trust in your everyday life? In this course, you will learn how to analyze and assess five common forms of inductive arguments: generalizations from samples, applications of generalizations, inference to the best explanation, arguments from analogy, and causal reasoning. The course closes by showing how you can use probability to help make decisions of all sorts.

Sep 14th 2026
4 Weeks
Introduction to Statistics & Data Analysis in Public Health (Coursera) Coursera
Imperial College London

Introduction to Statistics & Data Analysis in Public Health (Coursera)

This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series.

Sep 7th 2026
4 Weeks
Statistical Inference and Hypothesis Testing in Data Science Applications (Coursera) Coursera
University of Colorado Boulder

Statistical Inference and Hypothesis Testing in Data Science Applications (Coursera)

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse.

Sep 21st 2026
5-12 Weeks
Principles of fMRI 1 (Coursera) Coursera
Johns Hopkins University

Principles of fMRI 1 (Coursera)

Functional Magnetic Resonance Imaging (fMRI) is the most widely used technique for investigating the living, functioning human brain as people perform tasks and experience mental states. It is a convergence point for multidisciplinary work from many disciplines. Psychologists, statisticians, physicists, computer scientists, neuroscientists, medical researchers, behavioral scientists, engineers, public health researchers, biologists, and others are coming together to advance our understanding of the human mind and brain. This course covers the design, acquisition, and analysis of Functional Magnetic Resonance Imaging (fMRI) data, including psychological inference, MR Physics, K Space, experimental design, pre-processing of fMRI data, as well as Generalized Linear Models (GLM’s).

Sep 14th 2026
4 Weeks
Materials Data Sciences and Informatics (Coursera) Coursera
Georgia Institute of Technology

Materials Data Sciences and Informatics (Coursera)

This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts.

Sep 7th 2026
5-12 Weeks
Variable Selection, Model Validation, Nonlinear Regression (Coursera) Coursera
Illinois Tech

Variable Selection, Model Validation, Nonlinear Regression (Coursera)

If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.

Sep 14th 2026
4 Weeks
Data Science in Real Life (Coursera) Coursera
Johns Hopkins University

Data Science in Real Life (Coursera)

Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analytic plan was outlined prior to analysis and followed exactly. The conclusions were clear and actionable decisions were obvious. Has that every happened to you? Of course not. Data analysis in real life is messy. How does one manage a team facing real data analyses? In this one-week course, we contrast the ideal with what happens in real life. By contrasting the ideal, you will learn key concepts that will help you manage real life analyses.

Sep 7th 2026
1 Week