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Overview
Inferences about causation are of great importance in science, medicine, policy, and business.
Key Features
This Causal Inference 2 course offered by Coursera in partnership with Columbia University 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.
You will study advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models.
Programme Structure
Courses include:
- Mediation and Conditioning on Intermediate Outcomes
- Reframing the Problem of Mediation
- Identification of Controlled, Average Direct and Indirect Effects
- Instrumental Variables and the Complier Average Causal Effect
- Longitudinal Causal Inference
- Interference and Fixed Effects
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
- Self-paced
Campus Location
- Mountain View, United States
Disciplines
EconometricsWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- Advanced Level
- Designed for those already in the industry
- This course is aimed at advanced learners and graduate‑level students who want to develop a rigorous understanding of causal inference methods for analyzing cause‑and‑effect relationships in fields such as science, medicine, policy, and business.
Tuition Fees
Additional Details
Course is free for the first 7 days. After 7 days, the course can be accessed with the Coursera Plus Subscription
Funding
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