Lecture Materials
All PPTs uploaded (but not PDFs) can be used for educational purposes.
Session Introduction
[Prerequisite 1] Why is causal inference so challenging?
[Prerequisite 2] Purpose of causal inference
[Prerequisite 3] Endogeneity and causal inference
Module 1. Research Design for Causal Inference
[7/6, 10 am KST] Session 1. Potential Outcomes Framework: Causal Mindset
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Videos in Korean
[7/7, 10 am KST] Session 2. Overview of Research Design for Causal Inference
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
Deaton, A. and Cartwright, N., 2018. Understanding and Misunderstanding Randomized Controlled Trials. Social Science & Medicine, 210, pp.2-21. (Download)
Goldfarb, A. and Tucker, C.E., 2014. Conducting Research with Quasi-Experiments: A Guide for Marketers. Rotman School of Management Working Paper No. 2420920. (Download)
Videos in Korean
[Session 2-2] 인과추론의 정석: 무작위 통제실험 (Randomized Controlled Trial)
[Session 2-3] 실험 아닌, 실험 같은 준실험 (Quasi-Experiment)
[Session 2-4] 준실험 분석도구: 이중차분법 & 회귀불연속 (Difference-in-Differences & Regression Discontinuity)
[7/9, 11 am KST] Session 3. Causal Inference in Business Research (1) Randomized Experiment
Guest Speaker: Hyeokkoo Eric Kwon (Assistant Professor of Information Technology and Operations Management, Nanyang Technological University)
Guest Speaker: Nakyung Kyung (Assistant Professor of Information Systems, National University of Singapore)
Lecture Note 1 (PDF)
Lecture Note 2 (PDF)
Reading List
Ghose, A., Kwon, H.E., Lee, D. and Oh, W., 2019. Seizing the Commuting Moment: Contextual Targeting based on Mobile Transportation Apps. Information Systems Research, 30(1), pp.154-174. (Download)
Kyung, N., Kwon, H.E. and Ravichandran, T.R., 2021. Walk for Whom? The Effectiveness of Egoistic and Philanthropic Incentive Designs for Mobile Health Interventions. Working Paper. (Download)
Videos in Korean
[7/10, 10 am KST] Session 4. Causal Inference in Industry (1) A/B Test
Guest Speaker: Younseok Lee (Product Owner/Software Engineer, ex-Coupang)
Lecture Note (PDF)
Videos in Korean
[7/12, 10 am KST] Session 5. Causal Inference in Business Research (2) Quasi-Experiment
Guest Speaker: Yongjin Park (Assistant Professor of Information Systems, City University of Hong Kong)
Guest Speaker: Yoonseock Son (Assistant Professor of IT, Analytics, and Operations, University of Notre Dame)
Lecture Note 1 (PDF)
Lecture Note 2 (PDF)
Reading List
Park, Y., Bang, Y. and Ahn, J.H., 2020. How Does the Mobile Channel Reshape the Sales Distribution in E-Commerce?. Information Systems Research, 31(4), pp.1164-1182. (Download)
Son, Y.,Oh, W., and Im, I., 2021. The Voice of Commerce: How AI Speakers Reshape Digital Content Consumption and Preference. Working Paper. (Download)
Videos in Korean
[7/14, 10 am KST] Session 6. Configurational Approach for Causal Recipes with QCA
Guest Speaker: YoungKi Park (Associate Professor of Information Systems & Technology Management, George Washington University)
Lecture Note (PDF)
Reading List
Park, Y., Fiss, P.C. and El Sawy, O.A., 2020. Theorizing the Multiplicity of Digital Phenomena: The Ecology of Configurations, Causal Recipes, and Guidelines for Applying QCA. MIS Quarterly, 44(4), pp.1492-1520. (Download)
Park, Y. and Mithas, S., 2020. Organized Complexity of Digital Business Strategy: A Configurational Perspective. MIS Quarterly, 44(1), pp.85-127. (Download)
[7/16, 10 am KST] Session 7. Graphical Model of Causal Relationships
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
Pearl, J., 2019. The Seven Tools of Causal Inference, with Reflections on Machine Learning. Communications of the ACM, 62(3), pp.54-60. (Download)
Imbens, G.W., 2020. Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics. Journal of Economic Literature, 58(4), pp.1129-79. (Download)
Videos in Korean
[Session 7-1] 인과 그래프 (Causal Diagram)
[Session 7-2] 인과 그래프에서의 변수 통제방법
[Session 7-3] 인과 그래프에서의 인과추론 전략
[7/21, 10 am KST] Session 9. Instrumental Variables
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
Sovey, A.J. and Green, D.P., 2011. Instrumental Variables Estimation in Political Science: A Readers’ Guide. American Journal of Political Science, 55(1), pp.188-200. (Download)
Swanson, S.A. and Hernán, M.A., 2013. Commentary: How to Report Instrumental Variable Analyses. Epidemiology, 24(3), pp.370-374. (Download)
Videos in Korean
[7/23, 10 am KST] Session 10. Causal Inference in Biomedical Research: Meta-Analysis and Generalizability
Guest Speaker: Hwanhee Hong (Assistant Professor of Biostatistics, Duke University)
Lecture Note (PDF)
Reading List
Hong, H., Fu, H., Price, K.L. and Carlin, B.P., 2015. Incorporation of Individual‐Patient Data in Network Meta‐Analysis for Multiple Continuous Endpoints, with Application to Diabetes Treatment. Statistics in Medicine, 34(20), pp.2794-2819. (Download)
Susukida, R., Crum, R.M., Hong, H., Stuart, E.A. and Mojtabai, R., 2018. Comparing Pharmacological Treatments for Cocaine Dependence: Incorporation of Methods for Enhancing Generalizability in Meta‐Analytic Studies. International Journal of Methods in Psychiatric Research, 27(4), p.e1609. (Download)
Module 2. Machine Learning for Causal Inference
[7/26, 10 am KST] Session 11. Overview of Machine Learning for Causal Inference
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
Hernán, M.A., Hsu, J. and Healy, B., 2019. A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks. Chance, 32(1), pp.42-49. (Download)
Hofman, J.M., Watts, D.J., Athey, S., Garip, F., Griffiths, T.L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M.J., Vazire, S. and Vespignani, A., 2021. Integrating Explanation and Prediction in Computational Social Science. Nature, pp.1-8. (Download)
Mullainathan, S. and Spiess, J., 2017. Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), pp.87-106. (Download)
Videos in Korean
[Session 11-1] 인과추론과 예측 방법론의 차이
[Session 11-2] 실증연구에서의 빅데이터와 머신러닝의 역할
[7/28, 10 am KST] Session 12. Applications of Machine Learning in Marketing
Guest Speaker: Jongho Kim (PhD Student in Quant Marketing, Cornell University)
Lecture Note (PDF)
Videos in Korean
[7/30, 10 am KST] Session 13. Causal Applications of Machine Learning Models
Guest Speaker: Daehwan Ahn (Postdoctoral Researcher, The Wharton School of University of Pennsylvania)
Lecture Note (PDF)
Videos in Korean
[8/3, 11 am KST] Session 14. Heterogeneous Treatment Effect Estimation Using Machine Learning
Guest Speaker: Yejin Kim (Assistant Professor of Biomedical Informatics, UT Health)
Lecture Note (PDF)
Reading List
Curth, A. and Schaar, M., 2021. Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. In International Conference on Artificial Intelligence and Statistics (pp. 1810-1818). (Download)
Alaa, A. and Schaar, M., 2018. Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. In International Conference on Machine Learning (pp. 129-138). (Download)
Künzel, S.R., Sekhon, J.S., Bickel, P.J. and Yu, B., 2019. Metalearners for Estimating Heterogeneous Treatment Effects using Machine Learning. Proceedings of the National Academy of Sciences, 116(10), pp.4156-4165. (Download)
Videos in Korean
[Session 14-1] 신약 개발에서의 인과추론의 역할과 한계
[Session 14-2] 머신러닝을 활용한 이질적 인과관계 분석 (Heterogeneous Treatment Effect)
[8/4, 10 am KST] Session 15. Causal Decision Making and Prescriptive Analytics
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
McFowland, E., Gangarapu, S., Bapna, R. and Sun, T., 2021. A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects. MIS Quarterly. forthcoming. (Download)
Sloman, S.A. and Lagnado, D., 2015. Causality in Thought. Annual Review of Psychology, 66, pp.223-247. (Download)
Schölkopf, B., Locatello, F., Bauer, S., Ke, N.R., Kalchbrenner, N., Goyal, A. and Bengio, Y., 2021. Toward Causal Representation Learning. Proceedings of the IEEE, 109(5), pp.612-634. (Download)
Videos in Korean
[Session 15-1] 인과적 의사결정 (Causal Decision Making)
[8/6, 10 am KST] Session 16. Causal Inference under the Rubric of Structural Causal Model
Guest Speaker: Yonghan Jung (PhD Student in Computer Science, Purdue University)
Lecture Note (PDF / Full Version)
Reading List
Elias Bareinboim, Juan Correa, Duligur Ibeling, and Thomas Icard. 2021. "On Pearl’s Hierarchy and the Foundations of Causal Inference," ACM special volume in honor of Judea Pearl. (Download)
Videos in Korean
[8/11, 10 am KST] Session 18. Synthetic Control / Causal Discovery
Instructor: Jiyong Park (Assistant Professor of Information Systems, University of North Carolina at Greensboro)
Reading List
Abadie, A., 2021. Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects. Journal of Economic Literature, 59(2), pp.391-425. (Download)
Glymour, C., Zhang, K. and Spirtes, P., 2019. Review of Causal Discovery Methods Based on Graphical Models. Frontiers in Genetics, 10, p.524. (Download)
Videos in Korean
[Session 18-1] 가상의 통제집단 (Synthetic Control)