Scientific Coordination

This online short course uses causal graphs (or “directed acyclic graphs”, DAGs) as a remarkably simple, yet general and powerful framework to describe and discuss a large set of problems that empirical social scientists need to tackle. Is my question of interest descriptive or causal? How can I communicate my assumptions effectively to others, and can I test them? How can I tell correlation from causation? How do I choose control variables for my regression models? After discussing how DAGs can be used to answer these foundational questions, the course also covers basics of causal interaction and effect heterogeneity, causal mediation, nonresponse/selection bias (and adjustments for it) and, if time permits, instrumental variables and panel data analysis from a DAG perspective.

The detailed syllabus with course times and literature will be available soon.

Target group

Participants will find the course useful if:

they are interested in causal questions and want to understand the assumptions associated with regression control, mediation analysis and instrumental variables better:

they are interested in non-causal questions, want to use data suffering from nonresponse, and want to understand how to use causal assumptions in this case.

Learning objectives

By the end of the course participants will:

know how to use causal graphs to visualize causal assumptions, define quantities of interest, and to determine testability of assumptions via d-separation;

know how to graphically determine identification of causal and descriptive quantities like average causal effects, causal interaction, effect heterogeneity, natural direct and indirect effects, and population distributions from data with nonresponse;

know under what graphical assumptions instrumental variable and panel data analysis typically operate;

will have some basic knowledge about how all of this relates to implementation in standard statistical software.

Organizational Structure of the Course

This short course throughout will change between short lecture-style inputs and individual or small-group hands-on exercises supervised by the lecturer and a teaching assistant (4hrs/day). Participants are encouraged to bring their own research ideas to develop them further using the material from the class. The lecturer will also be available for individual consultation in the afternoons.

Prerequisites

Participants should be willing to learn and use formal reasoning and must have at least Bachelor-level knowledge of statistics. Basic knowledge of R is helpful.

Software and Hardware Requirements

We will briefly discuss some elements of the R packages “dagitty”, “sensemakr”, “mediation”, “AER”, “estimatr”, “PanelMatch”. Most of the course will not depend on using R. For those who have never used R, here are installation instructions and a short introductory video:

Downloading R and RStudio: https://rstudio-education.github.io/hopr/starting.html

For those who have never used R before (18 mins): https://www.youtube.com/watch?v=tvv4IA8PEzw

Basics of simulation in R (15 mins): https://www.youtube.com/watch?v=tvv4IA8PEzw