PhD Courses
The HCHE regularly offers courses for doctoral students in order to support young scientists. The courses usually last a few days to a week and are held by renowned, mostly international scientists.
In 2021 the following courses took place:
Supervised and Causal Machine Learning
Participants in this course learn common concepts and methods of Causal Machine Learning to analyze the effects of experimental or observational policy interventions. Causal Machine Learning combines two mature fields of data analytics. On the one hand, the field of machine learning (ML) has improved our ability to identify correlation patterns in data, which is important for making high-quality predictions. On the other hand, the field of causal inference has increased our knowledge of how to evaluate the impact of interventions, which is essential for high-quality decision making. The promise of causal machine learning is to deliver the best of both worlds, to draw (more reliable) and more informative causal inference.
oTree
This workshop is about developing experiments with oTree and is intended for people who have little or no experience with oTree. After the workshop, you will know everything you need to comfortably start developing and deploying your own experiments with oTree.
Measuring quality of care using administrative data (17.-19.09.2018)
This course is designed to help participants build and extend their understanding of measuring quality of care using administrative data. Through discussions and analyses of current conceptual and methodological developments in the quality measurement discipline and some of its main reference disciplines, participants will advance their skills of reflecting different approaches in measuring quality of care.
In terms of teaching formats, the course will be using a mixture of formats and approaches – from traditional lectures to interactive seminar sessions.
Instructor: Prof. Dr. Jonas Schreyögg, Prof. Gary Young, Prof. Dr. Eva Oppel
When: Monday, 17.09.2018, 9:00 am until Wednesday, 19.09.2018, 5:00 pm
Where: Hamburg Center for Health Economics, room 4029
For further information please click here.
Defining and measuring patient satisfaction: current issues and concepts (20.09.-21.09.2018)
Patients’ self-reported experience with care has become a widely used type of quality measure. This course is designed to help participants build and extend their understanding of the theoretical concepts and approaches to patients’ self-reported experience with care and to review several advances in the area. Through discussions and analyses of current theoretical and conceptual developments in the patient satisfaction research field and some of its main reference disciplines, participants advance their understanding of approaches to measuring patient satisfaction.
Instructor: Prof. Dr. Eva Oppel
When: Thursday, 20.09.2018, 9:00 am until Friday, 19.09.2018, 4:00 pm
Where: Hamburg Center for Health Economics, room 4029
For further information please click here.
Risk adjustment methods for quality of care outcomes with administrative data (24.09.-26.09.2018)
The course will cover methods for drawing causal inference in interventional, non-experimental/non-randomized studies on quality of care with administrative data. In order to control for confounders between intervention and control group, at first simple methods (such as stratification and standardization) as well as advanced methods (Propensity Score Matching, Difference-in-Differences, Regression-Discontinuity Designs) are taught. The course will also give an overview on common risk-adjustment instruments (generic and disease
specific risk-adjustment scores based on diagnoses or ATC codes) for use with health outcomes.
Instructors: Prof. Dr. Marco Caliendo / Prof. Dr. Tom Stargardt
When: Monday, 24.09.2018, 09:00 am until Wednesday, 26.09.2018, 5 pm
Where: Hamburg Center for Health Economics, room 4029
For further information please click here.
Organizing and Managing Hospital Services for Better Quality of Care: Theory and Methods (Cancelled!)
In the presence of rising health care costs, many countries are trying to determine the most efficient and effective ways to organize and manage hospital care, which constitutes a significant component of healthcare spending. This one-day course will offer participants an opportunity to discuss theories and related empirical research on this topic. Our discussion will be directed to the following questions.
- Should some types of hospital services be regionalized to ensure high patient volume for clinicians based on the proposition that high volume confers experience that promotes quality of care?
- Can smaller hospitals adequately compensate for lower volume by specializing in the delivery of certain services?
- How important are nurse staffing levels for ensuring high quality of care? Should minimum staffing levels be required?
- Do hospitals’ efforts to standardize care practices through technology and advanced management practices complement or substitute for some physicians’ patient care activities?
These questions implicate several theoretical perspectives from economics, organization theory, and human resource management that we will consider for their value in helping us study topics pertaining to the organization and management of hospital services. We will also critically examine several studies that seek to address these issues in terms of their conceptual frameworks and methodological approaches.
When: Tuesday, 05.05.2020, 09:00 until 17:00
Where: Hamburg Center for Health Economics
Supervised and Causal Machine Learning (31th May until 4th June 2021)
All spaces have been filled!
Participants of this course will learn popular concepts and methods in Causal Machine Learning methods to analyse effects of either experimental or observational policy interventions. Causal Machine Learning combines two mature fields in data analytics. On the one hand, the field of Machine Learning (ML) advanced our ability to detect correlational pattern in data, which is important to form high-quality predictions. On the other hand, the field of Causal Inference advanced our knowledge about how to assess the effects of interventions, which is essential for high-quality decision making. The promise of Causal Machine Learning is to deliver the best of both worlds to draw (more) reliable and more informative causal inference.
This course will focus on tools that are already mature in the sense that they are available for implementation for practitioners in the software R and covers three major topics:
- Introduction/recap of supervised ML with a focus on methods that are important ingredients of Causal ML
- Estimation of average effects in the presence of confounding
- Estimation of heterogeneous effects in experimental and observational setting
Instructor: Assistant Professor, PhD Michael C. Knaus, Swiss Institute for Empirical Economic Research of the University of St. Gallen
When: 31th May until 4th June 2021, 09:00 to 12:00
Where: Online via Zoom
Course value: Module „Econometrics“ (5 LP)
Further information can be found here.
oTree Workshop (24th until 25th June 2021)
All spaces have been filled!
This workshop is on how to develop experiments with oTree and it is aimed at people with no or little prior experience with oTree. After the workshop you know everything you need to comfortably start developing and deploying your own experiments with oTree.
Instructor: Jonas Frey, doctoral candidate at the Saïd Business School at the University of Oxford
When: 24th until 25th June 2021, 09:00 to 18:00
Where: Online via Zoom
Course value: Module „Econometrics“ (2 LP for WiSo, 2.5 LP for BWL)
Further information can be found here.
Current Topics in Experimental Health Economics (Cancelled!)
This seminar provides an introduction to the current research in health economics using experimental methods. The focus will be on laboratory experiments. We will discuss papers dealing with core issues in health economics, in particular, physician behavior, health insurance choice, provider competition and health care finance. The teachers will provide inputs regarding the theoretical background and experimental methods. Participants are expected to provide short introductions to papers as a starting point for discussions. The objective is to familiarize participants with the current state of research, to show the relationship between theory and experiment and to provide a basis for own research. For the course assessment, students will form small groups and develop an experimental design that will be presented at the end of the course.
Instructors: Prof. Mathias Kifmann, Prof. Johanna Kokot
When: Starts April 7th, tuesdays, 16:15 - 17:45 hrs
Where: Hamburg Center for Health Economics, Esplanade 36, Room 4029
For further information click here.
Regulatory Intervention in Health Care Markets (13.11.2018)
This one-day course addresses regulatory intervention in health care markets. It begins with an evaluation of historical developments that have shaped markets for health care and health insurance and assess the evolving roles of consumers, health care providers, employers, insurance companies, and regulators. After that, factors contributing to rising health care costs, decisions to purchase insurance, and the conduct and performance of the health insurance market will be explored.
Instructor: Professor Patricia Born, Florida State University
When: Thursday, 13.11.2018, 09:00 am until 5:00 pm
Where: Hamburg Center for Health Economics, room 4029
For further information please click here.