Causal Inference

This course introduces students to data and statistics. By the end of the course, students should be able to interpret descriptive statistics, causal analyses and visualizations to draw meaningful insights. The course first introduces a framework for thinking about the various purposes of statistical analysis. We’ll talk [...]

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 [...]

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 [...]

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 [...]

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are [...]

Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference. Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study [...]

How can we know if the differences in wages between men and women are caused by discrimination or differences in background characteristics? In this course we look at causal effects as opposed to spurious relationships. We will discuss how they can be identified in the social sciences using quantitative [...]

An introduction to machine learning for healthcare, ranging from theoretical considerations to understanding human consequences of deploying technology in the clinic, through hands-on Python projects using real healthcare data. Machine learning methods have revolutionized many aspects of healthcare, from new models that help clinicians make more informed decisions to [...]