Causal Inference Courses and Certifications

Causal Inference

Uncover cause-and-effect relationships in observational data using advanced methods like Difference-in-Differences, Synthetic Control, and Regression Discontinuity with Python.

• Udacity
• 13 hours
• On-Demand
• Paid Course

A look at using Instrumental Variables and Regression Discontinuity to estimate causal effects

• DataCamp
• On-Demand
• Free Trial Available

Welcome to the 3rd course in our series on causal inference concepts and methods created by Duke University with support from eBay, Inc.

• DataCamp
• On-Demand
• Free Trial Available

Welcome to the Causal Inference with R – Experiments, the 2nd of 7 courses on causal inference concepts and methods created by Duke University with support from eBay, Inc.

• DataCamp
• On-Demand
• Free Trial Available

Causal Inference Project Ideation

Master causal inference through field experiments and A/B testing, exploring ethical considerations, designing randomized trials, and analyzing observational data for data-driven organizational decision-making.

• Coursera
• 11 hours 44 minutes
• On-Demand
• Paid Course

Machine Learning and AI Foundations: Causal Inference and Modeling

136 ratings at LinkedIn Learning

Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.

• LinkedIn Learning
• 2 hours 51 minutes
• On-Demand
• Free Trial Available

Learn the Basics of Causal Inference with R

20 ratings at Codecademy

Learn conceptual foundations and practical techniques for determining causal relationships in data. Master matching, weighting, instrumental variables, and difference-in-differences methods to uncover why things happen.

• Codecademy
• 4 hours
• On-Demand
• Free Trial Available

Difference in Differences for Causal Inference

20 ratings at Codecademy

Estimate causal effects by analyzing trends over time. Learn to implement Difference in Differences technique, mimic experiments, and solve real-world problems using existing data.

• Codecademy
• 1 hour
• On-Demand
• Free Trial Available

Potential Outcomes Framework for Causal Inference

13 ratings at Codecademy

Learn to estimate unmeasurable outcomes using the Potential Outcomes Framework for causal inference. Develop counterfactual thinking and a formal language to determine why things happen.

• Codecademy
• 1 hour
• On-Demand
• Free Trial Available

Causal Inference with Survey Data

12 ratings at LinkedIn Learning

Explore the concepts of causal inference in survey data, learn some of the underlying theory of causality, and focus on empirical methods to identify causality in data.

• LinkedIn Learning
• 2 hours 9 minutes
• On-Demand
• Free Trial Available

Learn techniques for balancing data, estimating treatment effects, and maximizing sparse datasets in causal analysis. Master matching, weighting, propensity scores, and stratification to prepare data and estimate unobserved outcomes.

• Codecademy
• 2 hours
• On-Demand
• Free Trial Available

NIeCer 201: Causal Inference from Observational Studies [CAUSIT]

Gain insights into causal inference from observational studies, covering principles, study designs, statistical methods, validity threats, and causal diagrams for health researchers.

• Swayam
• On-Demand
• Free Online Course

Richard McElreath

Science Before Statistics - Causal Inference

Comprehensive workshop on causal inference, covering key concepts like causal design, graph analysis, and Bayesian inference. Equips researchers with tools to navigate complex causal relationships in data analysis.

• YouTube
• 3 hours 2 minutes
• On-Demand
• Free Video

Alan Turing Institute

From Causal Inference to Autoencoders, Memorization & Gene Regulation - Caroline Uhler, MIT

Exploring causal inference, autoencoders, and gene regulation through genomics and 3D genome organization. Integrates data modalities, analyzes overparameterized autoencoders, and links memorization to real-valued data retrieval.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Alan Turing Institute

Causal Inference in Observational Studies - Emma McCoy, Imperial College London

Explore causal inference in observational studies with Prof. Emma McCoy. Learn about robust estimation of treatment effects, time-series methodology, and applications in transport settings.

• YouTube
• 32 minutes
• On-Demand
• Free Video