Causal Inference from Coursera

This course offers a rigorous mathematical survey of 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.

We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course.

What's inside

Syllabus

MODULE 1: Key Ideas

Module 2: Randomization Inference

MODULE 3: Regression

Module 4: Propensity Score

Module 5: Matching

Module 6: Special Topics

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces students to the statistical literature on causal inference for the past 35-40 years, evolving data-driven decision making

Taught by Michael E. Sobel, an accomplished instructor

Establishes a strong foundation for Master's-level statisticians or researchers who use causal inference

Modules are broken down into smaller, digestible components to help students learn piecemeal

Assumes some fluency in statistics and advanced mathematical concepts, which may present a barrier to those who come into the course without preparation

Reviews summary

Varied course opinions about causal inference

learners say this course contains engaging assignments and a difficult exam. Some students may find the lectures to be difficult to follow and talking head videos may not be the best teaching style. Repeated errors in quizzes and lectures, vague assessment instructions, and lack of slides may hinder the learning experience. Despite these issues, the course covers a great amount of ground with an excellent selection of materials.

Lectures may be difficult to follow

"It is impossible to learn statistics without slides in the first week."

"Layout of slides is easy to lose people."

"Currently, however, there are many superficial problems with the course, including repeated errors in the quizzes and lectures that are confusing because the slides are missing."

Course contains repeated errors

"The layout of the slides is easy to lose people. There are too many errors in the quiz and no one has ever tried to correct them even though some students have been pointing them out for years."

"Assignments are a mess, and apparently haven't been fixed for years after multiple complaints."

"Assessment had multiple errors and vague instructions."

Talking head lectures

"Talking head is not the best way to present for presenting such subjects."

"Instructor does not use Notes, Whiteboard, etc. to demonstrate points. Written elaboration precedes verbal explanation in a statistics course!"

"A talking lecturing on mathematics and statistics without any equations or slides. It's impossible to follow and the worst form of pedagogy."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Causal Inference with these activities:

Review Probability and Statistics Concepts

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Establish a solid foundation by refreshing probability and statistics concepts.

Browse courses on Probability

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Review probability concepts such as random variables, distributions, and conditional probability.
Go through basic statistics concepts, including descriptive statistics, hypothesis testing, and regression analysis.

Read 'Causal Inference in Statistics: A Primer'

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Acquire a comprehensive understanding of causal inference concepts through a structured reading.

View Causal Inference in Statistics - A Primer on Amazon

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Read the book thoroughly, taking notes and highlighting key concepts.
Summarize the main ideas of each chapter in your own words.
Complete the exercises and discussion questions at the end of each chapter.

Review Causality Theory Research Papers

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Develop a holistic understanding of causal inference theory by compiling research papers.

Browse courses on Causal Inference

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Identify peer-reviewed research papers on causal inference theory.
Read and summarize the key findings of each paper.
Organize the papers into a cohesive compilation.
Include annotations and reflections on the strengths and weaknesses of each paper.

One other activity

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Show all four activities

Practice Propensity Score Matching

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Gain proficiency in propensity score matching, a technique for reducing bias.

Browse courses on Propensity Score Matching

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Load datasets and pre-process data to prepare for propensity score matching.
Estimate propensity scores using logistic regression or other appropriate methods.
Match treated and control groups based on their propensity scores.
Analyze the matched data to evaluate the effectiveness of the matching.

Career center

Learners who complete Causal Inference will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians collect, analyze, interpret, and present data. They use their findings to help organizations make informed decisions. A course on causal inference would help a Statistician understand how to design studies, collect data, and analyze results in order to make valid conclusions about the relationships between variables. It would also provide them with the tools and techniques they need to communicate their findings effectively.

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Data Scientist

Data Scientists study data patterns and trends to extract meaningful insights. They use statistical models and machine learning to help organizations make data-driven decisions. A course on causal inference would help Data Scientists understand the relationship between variables and draw conclusions from data. This course teaches the statistical literature that can be used to infer causation, helping Data Scientists make more accurate predictions and build more effective models.

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Epidemiologist

Epidemiologists investigate the causes of disease and other health problems in populations. They use their findings to develop and evaluate public health programs. A course on causal inference would help an Epidemiologist understand the relationship between risk factors and health outcomes. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of public health interventions.

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Public Health Nurse

Public Health Nurses work to improve the health of communities. They use their knowledge of public health to help people prevent disease and promote healthy lifestyles. A course on causal inference would help a Public Health Nurse understand the relationship between health behaviors and health outcomes. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of public health interventions.

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Machine Learning Engineer

Machine Learning Engineers build, deploy, and maintain machine learning models. They use their expertise in statistics, computer science, and engineering to develop models that can learn from data and make predictions. A course on causal inference would help a Machine Learning Engineer understand the relationship between the features of data and the target variable. It would also provide them with the tools and techniques they need to develop models that are robust to noise and bias.

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Actuary

Actuaries use mathematical and statistical methods to assess risk. They use their expertise to help insurance companies, pension plans, and other organizations make decisions about financial risk. A course on causal inference would help an Actuary understand the relationship between risk factors and insurance claims. It would also provide them with the tools and techniques they need to develop models that can predict the likelihood of insurance claims.

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Social Worker

Social Workers help people overcome social and economic problems. They use their knowledge of social work to help individuals, families, and communities. A course on causal inference would help a Social Worker understand the relationship between social factors and well-being. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of social work interventions.

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Economist

Economists study the production, distribution, and consumption of goods and services. They use their knowledge of economics to help businesses and governments make decisions about resource allocation. A course on causal inference would help an Economist understand the relationship between economic variables. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of economic policies.

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Health Policy Analyst

Health Policy Analysts study the development and implementation of health policy. They use their knowledge of health policy to help policymakers make decisions about the allocation of resources. A course on causal inference would help a Health Policy Analyst understand the relationship between health policy and health outcomes. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of health policies.

See salaries and explore the career path for Health Policy Analyst

Biostatistician

Biostatisticians apply statistical methods to the study of biological data. They use their expertise to help researchers design and conduct studies, analyze data, and interpret results. A course on causal inference would help a Biostatistician understand the relationship between biological variables and health outcomes. It would also provide them with the tools and techniques they need to design and conduct studies that can provide valid evidence about the effectiveness of medical treatments.

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Data Analyst

Data Analysts collect, clean, and analyze data. They use their findings to help businesses make informed decisions. A course on causal inference may be useful for a Data Analyst who wants to understand the relationship between variables and draw conclusions from data. It would also provide them with the tools and techniques they need to communicate their findings effectively.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of computer science to solve problems and create new technologies. A course on causal inference may be useful for a Software Engineer who wants to understand the relationship between software design and software performance. It would also provide them with the tools and techniques they need to develop software that is robust and efficient.

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Operations Research Analyst

Operations Research Analysts use mathematical and statistical methods to solve problems in business and industry. They use their expertise to help organizations improve their efficiency and productivity. A course on causal inference may be useful for an Operations Research Analyst who wants to understand the relationship between business decisions and business outcomes. It would also provide them with the tools and techniques they need to develop models that can predict the impact of business decisions.

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Computer Scientist

Computer Scientists study the theory and practice of computing. They use their knowledge of computer science to develop new technologies and solve problems. A course on causal inference may be useful for a Computer Scientist who wants to understand the relationship between algorithms and data. It would also provide them with the tools and techniques they need to develop algorithms that are efficient and accurate.

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Management Consultant

Management Consultants help businesses improve their performance. They use their knowledge of business and management to help businesses identify problems and develop solutions. A course on causal inference may be useful for a Management Consultant who wants to understand the relationship between management practices and business outcomes. It would also provide them with the tools and techniques they need to develop recommendations that can help businesses improve their performance.

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