A Robust Optimization Approach to Reliable Statistical Inference with Variables Generated by Machine Learning

References

• Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Inform. Systems Res. 35(2):441–459.Link, Google Scholar
• Bertsimas D, Nohadani O (2019) Robust maximum likelihood estimation. INFORMS J. Comput. 31(3):445–458.Link, Google Scholar
• Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.Crossref, Google Scholar
• Bertsimas D, Den Hertog D, Pauphilet J (2021) Probabilistic guarantees in robust optimization. SIAM J. Optim. 31(4):2893–2920.Crossref, Google Scholar
• Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math. Programming 167:235–292.Crossref, Google Scholar
• Bertsimas D, Dunn J, Pawlowski C, Zhuo YD (2019) Robust classification. INFORMS J. Optim. 1(1):2–34.Link, Google Scholar
• Bound J, Brown C, Duncan GJ, Rodgers WL (1994) Evidence on the validity of cross-sectional and longitudinal labor market data. J. Labor Econom. 12(3):345–368.Crossref, Google Scholar
• Buonaccorsi JP (2010) Measurement Error: Models, Methods, and Applications (Chapman and Hall/CRC, New York).Crossref, Google Scholar
• Carroll RJ, Stefanski LA (1994) Measurement error, instrumental variables and corrections for attenuation with applications to meta-analyses. Statist. Medicine 13(12):1265–1282.Crossref, Google Scholar
• Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement Error in Nonlinear Models: A Modern Perspective (Chapman and Hall/CRC, New York).Crossref, Google Scholar
• Chan J, Wang J (2018) Hiring preferences in online labor markets: Evidence of a female hiring bias. Management Sci. 64(7):2973–2994.Link, Google Scholar
• Chen R, Paschalidis IC (2018) A robust learning approach for regression models based on distributionally robust optimization. J. Machine Learn. Res. 19(13):1–48. Google Scholar
• Delage E, Ye Y (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.Link, Google Scholar
• El Ghaoui L, Lebret H (1997) Robust solutions to least-squares problems with uncertain data. SIAM J. Matrix Anal. Appl. 18(4):1035–1064.Crossref, Google Scholar
• Fong C, Tyler M (2021) Machine learning predictions as regression covariates. Political Anal. 29(4):467–484.Crossref, Google Scholar
• Fuller WA (2009) Measurement Error Models (John Wiley & Sons, New York).Google Scholar
• Gu B, Konana P, Rajagopalan B, Chen HWM (2007) Competition among virtual communities and user valuation: The case of investing-related communities. Inform. Systems Res. 18(1):68–85.Link, Google Scholar
• Hong LJ, Huang Z, Lam H (2021) Learning-based robust optimization: Procedures and statistical guarantees. Management Sci. 67(6):3447–3467.Link, Google Scholar
• Jockers M (2017) Package “syuzhet.” https://cran.r-project.org/web/packages/syuzhet.Google Scholar
• Kuhn D, Esfahani PM, Nguyen VA, Shafieezadeh-Abadeh S (2019) Wasserstein distributionally robust optimization: Theory and applications in machine learning. INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 130–166.Link, Google Scholar
• Lee D, Hosanagar K, Nair HS (2018) Advertising content and consumer engagement on social media: Evidence from Facebook. Management Sci. 64(11):5105–5131.Link, Google Scholar
• McAuley JJ, Leskovec J (2013) From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews. Proc. 22nd Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 897–908.Google Scholar
• Meijer E, Oczkowski E, Wansbeek T (2021) How measurement error affects inference in linear regression. Empirical Econom. 60(1):131–155.Crossref, Google Scholar
• Mohammad S, Turney P (2010) Emotions evoked by common words and phrases: Using Mechanical Turk to create an emotion lexicon. Proc. NAACL HLT 2010 Workshop Comput. Approaches Anal. Generation Emotion Text (Association for Computational Linguistics, Stroudsburg, PA), 26–34.Google Scholar
• Qiao M, Huang KW (2021) Correcting misclassification bias in regression models with variables generated via data mining. Inform. Systems Res. 32(2):462–480.Link, Google Scholar
• Schecter A, Nohadani O, Contractor N (2022) A robust inference method for decision making in networks. MIS Quart. 46(2):713–738.Crossref, Google Scholar
• Tirunillai S, Tellis GJ (2012) Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Sci. 31(2):198–215.Link, Google Scholar
• Wooldridge JM (2010) Econometric Analysis of Cross Section and Panel Data (MIT Press, Cambridge, MA).Google Scholar
• Yang M, Adomavicius G, Burtch G, Ren Y (2018) Mind the gap: Accounting for measurement error and misclassification in variables generated via data mining. Inform. Systems Res. 29(1):4–24.Link, Google Scholar
• Yang M, McFowland E III, Burtch G, Adomavicius G (2022) Achieving reliable causal inference with data-mined variables: A random forest approach to the measurement error problem. INFORMS J. Data Sci. 1(2):138–155.Link, Google Scholar
• Zhang S, Lee D, Singh PV, Srinivasan K (2022) What makes a good image? Airbnb demand analytics leveraging interpretable image features. Management Sci. 68(8):5644–5666.Link, Google Scholar