Module 1

Research Design for Data Analytics

Topics

Materials

Session Introduction

Slide Download (pdf / ppt)

1st Session

(6/25, 14:00~15:30)

Two Paradigms of Analytics: Input-Output Framework

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Chetty, R. and Hendren, N., 2018. The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects. Quarterly Journal of Economics, forthcoming.
Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B. and Ermon, S., 2016. Combining Satellite Imagery and Machine Learning to Predict Poverty. Science, 353(6301), pp.790-794.

(Optional)

Ghose, A. and Ipeirotis, P.G., 2011. Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), pp.1498-1512.
Varian, H.R., 2016. Causal Inference in Economics and Marketing. Proceedings of the National Academy of Sciences (PNAS), 113(27), pp.7310-7315.

2nd Session

(6/28, 14:00~15:30)

The Right Tool for the Right Question

Why is Predictive Analytics not Well-Suited for Causal Inference?
Why is Identification Strategy not Well-Suited for Prediction?

Slide Download (pdf / ppt)
OLS Simulation (xls)
Mullainathan, S. and Spiess, J., 2017. Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), pp.87-106.
Blake, T., Nosko, C. and Tadelis, S., 2015. Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment. Econometrica, 83(1), pp.155-174.

(Optional)

Bapna, R. and Umyarov, A., 2015. Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks. Management Science, 61(8), pp.1902-1920.
Goel, S. and Goldstein, D.G., 2013. Predicting Individual Behavior with Social Networks. Marketing Science, 33(1), pp.82-93.

3rd Session

(7/2, 14:00~15:30)

Causal Inference

: Identification Strategy (1)

Randomized Experiment
Quasi-Experiment

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[Randomized Experiment] Bloom, N., Liang, J., Roberts, J. and Ying, Z.J., 2015. Does Working from Home Work? Evidence from a Chinese Experiment. Quarterly Journal of Economics, 130(1), pp.165-218.
[Difference-in-Differences] Greenwood, B.N. and Wattal, S., 2017. Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities. MIS Quarterly, 41(1), pp.163-187.

(Optional)

[Difference-in-Differences + Matching] Rishika, R., Kumar, A., Janakiraman, R. and Bezawada, R., 2013. The Effect of Customers' Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation. Information Systems Research, 24(1), pp.108-127.
[Regression Discontinuity] Fredriksson, P., Öckert, B. and Oosterbeek, H., 2012. Long-Term Effects of Class Size. Quarterly Journal of Economics, 128(1), pp.249-285.

4th Session

(7/5, 14:00~15:30)

Causal Inference

: Identification Strategy (2)

Instrument Variable Approach
Dynamic Panel Model (Internal Instruments)

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[Instrument Variable] Acemoglu, D. and Johnson, S., 2005. Unbundling Institutions. Journal of Political Economy, 113(5), pp.949-995.
[Instrument Variable] Park, J., Kim, J., Pang, M. and Lee, B., 2018. The Deterrent Effect of Ride-Sharing on Sexual Assault and Investigation of Situational Contingencies. KAIST Working Paper. (download)

(Optional)

[Dynamic Panel Model] Park, J., Han, K. and Lee, B., 2018. An Empirical Analysis of Cloud Computing and Energy Efficiency: A Stochastic Frontier Approach. KAIST Working Paper. (download)
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.

5th Session

(7/9, 14:00~15:30)

Reflection on Predictive Analytics

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Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J. and Mullainathan, S., 2017. Human Decisions and Machine Predictions. Quarterly Journal of Economics, 133(1), pp.237-293. (For a friendly version of this paper, I recommend reading a HBR article "A Guide to Solving Social Problems with Machine Learning")
Lambrecht, A. and Tucker, C., 2019. Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads. Management Science. forthcoming

(Optional)

Lazer, D., Kennedy, R., King, G. and Vespignani, A., 2014. The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), pp.1203-1205.
Sweeney, L., 2013. Discrimination in Online Ad Delivery. Communications of the ACM, 56(5), pp.44-54.

6th Session

(7/12, 14:00~15:30)

The Art of Prescriptive Analytics: Predictive Analytics + Causal Inference + Optimization

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Bertsimas, D., O’Hair, A., Relyea, S. and Silberholz, J., 2016. An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer. Management Science, 62(5), pp.1511-1531.
Park, J. and Kim, J., 2018. Leveraging Machine Learning to Reduce Racial Bias on Online Platforms: A Neural Machine Translation Approach. KAIST Working Paper. (download)

(Optional)

Ren, K., Zhang, W., Chang, K., Rong, Y., Yu, Y. and Wang, J., 2018. Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising. IEEE Transactions on Knowledge and Data Engineering, 30(4), pp.645-659.
Samorani, M. and LaGanga, L.R., 2015. Outpatient Appointment Scheduling Given Individual Day-Dependent No-Show Predictions. European Journal of Operational Research, 240(1), pp.245-257.

7th Session

(7/16, 14:00~15:30)

Computational Social Science: Computer Science for Social Science

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Tambe, P., 2014. Big Data Investment, Skills, and Firm Value. Management Science, 60(6), pp.1452-1469.
Aral, S. and Walker, D., 2014. Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment. Management Science, 60(6), pp.1352-1370.

(Optional)

Vosoughi, S., Roy, D. and Aral, S., 2018. The Spread of True and False News Online. Science, 359(6380), pp.1146-1151.
Einav, L. and Levin, J., 2014. Economics in the Age of Big Data. Science, 346(6210), p.1243089.

8th Session

(7/19, 14:00~15:30)

Economining: Data Mining and Machine Learning for Empirical Research

Slide Download (pdf / ppt)
Zhang, S., Lee, D., Singh, P. V., and Srinivasan, K., 2017, How Much is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics. CMU Working Paper. Available at SSRN: https://ssrn.com/abstract=2976021
Park, J., Kim, J., Cho, D. and Lee, B., 2018. Pitching with Style: The Role of the Entrepreneur's Video Pitch on Online Crowdfunding. KAIST Working Paper. (download)

(Optional)

Ghose, A., Ipeirotis, P.G. and Li, B., 2012. Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content. Marketing Science, 31(3), pp.493-520.
Wang, Q., Li, B. and Singh, P., 2018. Copycats vs. Original Mobile Apps: A Machine Learning Copycat Detection Method and Empirical Analysis. Information Systems Research. forthcoming

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