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CERTIFICATE
Causality: Inferring Causal Effects from Observational Data
Learn to define, analyze, and estimate causal effects using modern statistical methods for observational data.
Course Cost
₹ 2,699
Intermediate
Skill Level
16 Hours
Self-paced lessons
This intermediate-level course introduces learners to the fundamental concepts and methods of causal inference. Over five weeks, you'll explore how causal effects are defined using potential outcomes, learn to express assumptions with causal graphs, and implement popular statistical methods such as matching, instrumental variables, and inverse probability of treatment weighting. The course emphasizes practical application, with opportunities to use R for data analysis. By the end, you'll be equipped to identify and estimate causal effects in various fields of study, moving beyond mere correlation to understand true causation.
42,529 Enrolled
English
What you'll learn
Define causal effects using potential outcomes
Distinguish between association and causation
Express assumptions using causal graphs (DAGs)
Implement matching and propensity score methods
Apply inverse probability of treatment weighting (IPTW)
Understand and use instrumental variables
Conduct causal inference analyses using R
Interpret results and assess the validity of causal estimates
Skills you'll gain
This course includes:
10.5 Hours PreRecorded video
16 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course on causal inference equips learners with essential skills to analyze and interpret causal relationships in observational data. Through a structured curriculum, students explore key concepts such as potential outcomes, directed acyclic graphs, and various statistical methods for estimating causal effects. The course emphasizes both theoretical understanding and practical application, with hands-on exercises using R. Topics covered include confounding, matching techniques, propensity scores, inverse probability weighting, and instrumental variables. By the end of the course, learners will be able to design and implement causal inference analyses, critically evaluate causal claims, and apply these methods across various disciplines.
Welcome and Introduction to Causal Effects
Module 1 · 3 Hours to complete
Confounding and Directed Acyclic Graphs (DAGs)
Module 2 · 2 Hours to complete
Matching and Propensity Scores
Module 3 · 5 Hours to complete
Inverse Probability of Treatment Weighting (IPTW)
Module 4 · 3 Hours to complete
Instrumental Variables Methods
Module 5 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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43,109
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Frequently asked Questions
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