Coursera

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

Python and Statistics for Financial Analysis (Coursera) Coursera
The Hong Kong University of Science and Technology - HKUST

Python and Statistics for Financial Analysis (Coursera)

Economics & Finance Business

Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

Sep 7th 2026
4 Weeks
Python Statistics Inference
Details
Mathematics for Machine Learning: PCA (Coursera) Coursera
Imperial College London

Mathematics for Machine Learning: PCA (Coursera)

Data Science

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

Sep 7th 2026
4 Weeks
Statistics Analysis Machine Learning
Details
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)

Business

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
Probability Excel Inference
Details
Estadística aplicada a los negocios (Coursera) Coursera
Universidad Austral

Estadística aplicada a los negocios (Coursera)

Statistics & Data Analysis Data Science

La toma de decisiones está en la esencia de los negocios. Gerenciar es tomar decisiones, muchas veces bajo presión, con información desordenada y en un contexto de incertidumbre. Un aspecto básico es entender y analizar la información, organizar los datos de forma de facilitar su posterior uso y la toma de decisiones.

Sep 14th 2026
4 Weeks
Business Statistics Decision Making
Details
Data Science in Real Life (Coursera) Coursera
Johns Hopkins University

Data Science in Real Life (Coursera)

Management & Leadership Statistics & Data Analysis

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
Statistics Machine Learning Data Management
Details
Fundamentals of Engineering Exam Review (Coursera) Coursera
Georgia Institute of Technology

Fundamentals of Engineering Exam Review (Coursera)

Engineering Sci: Physics

The purpose of this course is to review the material covered in the Fundamentals of Engineering (FE) exam to enable the student to pass it. It will be presented in modules corresponding to the FE topics, particularly those in Civil and Mechanical Engineering. Each module will review main concepts, illustrate them with examples, and provide extensive practice problems.

Sep 7th 2026
5-12 Weeks
Math Statistics Probability
Details
Statistics and Data Analysis with Excel, Part 2 (Coursera) Coursera
University of Colorado Boulder

Statistics and Data Analysis with Excel, Part 2 (Coursera)

Statistics & Data Analysis Data Science

This course is meant to be a direct continuation of "Statistics and Data Analysis with Excel, Part 1." Therefore, it is not recommended to take Part 2 unless you've also taken Part 1. Building on the topics learned in Part 1 of the course (probability, probability mass and density functions, the normal and standard normal distributions), this course dives into a more applied side of statistics.

Sep 21st 2026
5-12 Weeks
Statistics Excel Data Analysis
Details
Improving Your Statistical Questions (Coursera) Coursera
Eindhoven University of Technology

Improving Your Statistical Questions (Coursera)

Statistics & Data Analysis Data Science

This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions.

Sep 7th 2026
5-12 Weeks
Statistics Statistical Inference Meta-Analysis
Details
Materials Data Sciences and Informatics (Coursera) Coursera
Georgia Institute of Technology

Materials Data Sciences and Informatics (Coursera)

Engineering Sci: Chemistry

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
Statistics Informatics Chemistry
Details