#### Causal Inference in Management and Economics Research

This two-day seminar provides an overview about methods for causal inference in empirical economic research. Starting with a general discussion about the concept of causality and its importance for addressing management and policy questions, it covers the most widely applied identification strategies in management, economics and computer science. Accompanying examples in Stata and R illustrate the discussed estimation techniques in a hands-on fashion. Due to its conceptual focus, the course is suitable for students with an intermediate level of knowledge in statistics and econometrics.

Lecture 1: The fundamental problem of causal inference (Slides)

Lecture 2: Dealing with confounding bias (Slides, Stata code)

Lecture 3: Identification with the help of surrogate experiments (Slides, Stata code)

Lecture 4: Regression discontinuity designs (Slides, Stata code)

Lecture 5: Using longitudinal data for causal inference (Slides, Stata code)

Lecture 6: Causal mediation analysis (Slides, R code)

**Causal Inference with Directed Acyclic Graphs**

This workshop provides an introduction to causal inference with directed acyclic graphs (DAG). DAGs combine techniques from graph theory with statistical probability concepts and offer powerful tools for causal reasoning. Originally developed in the computer science and artificial intelligence field, the approach gets more and more traction in other disciplines too, such as epidemiology, sociology, finance, and economics. DAGs allow to check the identifiably of causal effects from observed data and lay bare the assumptions necessary for causal inference by relying on intuitive graphical criteria, which do not require a lot of algebra. As an encompassing framework for causal inference, DAGs also help to understand the identification arguments behind other popular estimation techniques such as regression, matching, instrumental variables and selection models. This workshop has no particular prerequisites. However, a good working knowledge in econometrics and probability theory is advantageous. (Slides, R code)

#### Causal Data Science with Directed Acyclic Graphs (DAGs)

*June 25-26, 2019, Goethe-University Frankfurt*

This 2-day workshop provides an introduction to causal data science with directed acyclic graphs (DAG). DAGs combine techniques from graph theory with statistical probability concepts and offer powerful tools for causal reasoning. Originally developed in the computer science and artificial intelligence field, they get more and more traction in other disciplines too (such as machine learning, philosophy, economics, finance, health sciences, etc.). DAGs allow to check the identifiably of causal effects from observed data and lay bare the assumptions necessary for causal inference by relying on intuitive graphical criteria, which do not require a lot of algebra. As an encompassing framework for causal inference, DAGs also help to understand the identification arguments behind other popular estimation techniques such as regression, matching, instrumental variables and selection models.

**Session 1:** Structural Causal Models, Interventions, and Graphs (Slides)

**Session 2:** Causal Discovery (Slides)

**Session 3:** Confounding Bias and Surrogate Experiments (Slides)

**Session 4:** Recovering from Selection Bias (Slides)

**Session 5:** Mediation Analysis (Slides)

**Session 6:** Transportability of Causal Knowledge Across Domains (Slides)