- applying [[25_TEPEI_empirical]], i organized empirical tom class https://github.com/Data4DM/BayesSD/discussions/181 a - [link](marginnote3app://note/67902F2C-0C92-4FDA-9345-B96FE574CB34) to ![[Pasted image 20241103080347.png|199]] | Topic | Year | Author(s) | Title | Link | Takeaway | | ----------------------------------------------------- | ---- | ----------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------- | -------- | | **1. Introduction to Empirical TOM** | | | | | | | | 2007 | Fisher, M. | Strengthening the Empirical Base of Operations Management | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/msom.1070.0168) | | | | 2013 | Van Mieghem, J.A. | OM Forum—Three Rs of Operations Management: Research, Relevance, and Rewards | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/msom.1120.0422) | | | | 2014 | Simchi-Levi, D. | OM Forum—OM Research: From Problem-Driven to Data-Driven | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/msom.2013.0471) | | | | 2019 | Terwiesch, C. | Empirical Research in Operations Management: From Field Studies to Analyzing Digital Exhaust | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/msom.2018.0723) | | | | 2020 | Terwiesch, C., Olivares, M., Staats, B.R., Gaur, V. | OM Forum—A Review of Empirical Operations Management over the Last Two Decades | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/msom.2018.0755) | | | | 2017 | Song, H., Tucker, A.L., Murrell, K.L., Vinson, D.R. | Closing the Productivity Gap: Improving Worker Productivity through Public Relative Performance Feedback and Validation of Best Practices | [Link](https://pubsonline.informs.org/doi/pdf/10.1287/mnsc.2017.2745) | | | **2. Machine Learning** | | | | | | | | 2010 | Shmueli, G. | To Explain or To Predict? | [Link](https://projecteuclid.org/journals/statistical-science/volume-25/issue-3/To-Explain-or-to-Predict/10.1214/10-STS330.full) | | | | 2016 | Ferreira, K.J., Lee, B.H.A., Simchi-Levi, D. | Analytics for an Online Retailer: Demand Forecasting and Price Optimization | [Link](https://pubsonline.informs.org/doi/abs/10.1287/msom.2015.0565) | | | | 2019 | Mejia, J., Mankad, S., Gopal, A. | A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections | [Link](https://pubsonline.informs.org/doi/10.1287/isre.2019.0866) | | | | 2022 | Ferreira, K.J., Parthasarathy, S., Sekar, S. | Learning to Rank an Assortment of Products | [Link](https://pubsonline.informs.org/doi/10.1287/mnsc.2021.4130) | | | | 2022 | Bertsimas, D., Pauphilet, J., Stevens, J., Tandon, M. | Predicting Inpatient Flow at a Major Hospital Using Interpretable Analytics | [Link](https://pubsonline.informs.org/doi/10.1287/msom.2021.0971) | | | **3. Observational Studies & Instrumental Variables** | | | | | | | | 1996 | Angrist, J.D., Imbens, G.W., Rubin, D.B. | Identification of Causal Effects Using Instrumental Variables | [Link](https://www.jstor.org/stable/2291629) | | | | 1991 | Angrist, J.D., Krueger, A.B. | Does Compulsory School Attendance Affect Schooling and Earnings? | [Link](https://www.jstor.org/stable/2937914) | | | | 2004 | Imbens, G.W. | Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review | [Link](https://www.jstor.org/stable/4134930) | | | | 2014 | Wooldridge, J.M. | Introduction to Econometrics: A Modern Approach (Chapters on Instrumental Variables) | [Link](https://www.cengage.com/c/introduction-to-econometricse-a-modern-approach-6e-wooldridge/) | | | **4. Presenting Research Ideas** | | | | | | | | | | | | | | | | | | | | | | | | | | | | **5. Field Experiments** | | | | | | | | | | | | | | | | | | | | | | | | | | | | **6. Lab Experiments** | | | | | | | | | | | | | | | | | | | | | | | | | | | | **7. Simulations** | | | | | | | | | | | | | | | | | | | | | | | | | | | | **8. Differences in Differences** | | | | | | | | | | | | | | | | | | | | | | | | | | | | **9. Regression Discontinuity & Matching** | | | | | | | | | | | | | | | | | | | | | | | | | | | ### GOALS This course has four main goals. First, this course is designed to equip you with a basic skill set of empirical methods for use in technology and operations management (and interdisciplinary research of operations management and information systems, marketing and other areas). The skill set refers to a variety of methods commonly employed in empirical research. The sessions are designed to make you feel comfortable with the basic aspects of the methodologies used in the papers, even if you have limited background in econometrics, machine learning, statistics, or other methodologies. However, if you plan to do empirical research, you should definitely take econometric and other methods courses and applied empirical courses from other departments as well. Second, the course aims to provide you with a working knowledge of some of the most well-known and recent empirical research in technology and operations management. The goal is to expose you to some well-implemented and well-known examples for each method, and to the frontiers of empirical technology and operations management research. You will be required to read and lead some paper discussions. This exercise will reinforce the understanding of the material and provide opportunities to practice presenting to an audience. The third (and probably the most important) goal of this course is to help you develop taste in research and develop your ability to conduct interesting research, which is also the goal of doctoral training. During your courses, you will build a strong methodological foundation if you work hard enough and we teach carefully. However, a good taste in research cannot be taught in a lecture, and we aim to help you develop it in this seminar-style course. You are in a doctoral program not to learn knowledge, but to create knowledge. In this course we will try to advance in this dimension by systematically discussing research ideas and aspects related to building research taste and developing the “craft” of research, and by giving feedback on a research project that you have to develop during the course (ideally, this could evolve to eventually become one of your dissertation essays). You should also develop the habit of consulting trusted audiences (your colleagues and faculty) once you come up with a research idea before you choose to invest significant time and energy into it. Finally, along the way we will try to discuss some institutional details of the academic community in technology and operations management. For example, what type of research different outlets publish, how the refereeing process work, the main conferences, job market, etc. (of course, this does not substitute the valuable information that you can obtain from your advisors and doctoral coordinator!). What this course is NOT: This course is not intended to replace the training you should be receiving from standard econometric courses. Due to the limited time we have, we will move rather quickly through the material.