Prediction Policy Problems

The Elements of Statistical Learning

Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970

Human Decisions and Machine Predictions

Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040

Can You Recognize an Effective Teacher When You Recruit One?

Rockoff, J. E., Jacob, B. A., Kane, T. J., & Staiger, D. O. (2011) · Education Finance and Policy · doi:10.1162/edfp_a_00022 · Cited by 390

Agent-based modelling of consumer energy choices

Rai, V., & Henry, A. D. (2016) · Nature Climate Change · doi:10.1038/nclimate2967 · Cited by 252

Productivity and Selection of Human Capital with Machine Learning

Chalfin, A. J., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., & Mullainathan, S. (2016) · American Economic Review · doi:10.1257/aer.p20161029 · Cited by 190

Joint Replacement Surgery in Elderly Patients With Severe Osteoarthritis of the Hip or Knee Decision Making, Postoperative Recovery, and Clinical Outcomes

Hamel, M. B. (2008) · Archives of Internal Medicine · doi:10.1001/archinte.168.13.1430 · Cited by 177

Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

Kang, J. R. W., Kuznetsova, P., Luca, M., & Choi, Y. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1150 · Cited by 89

Predicting and Preventing Shootings among At-Risk Youth

Chandler, D. R., Levitt, S. D., & List, J. A. (2011) · American Economic Review · doi:10.1257/aer.101.3.288 · Cited by 60

Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online ReviewsPreprint

Kang, J. R. W., Kuznetsova, P., Choi, Y., & Luca, M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2293165 · Cited by 42

Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks

Taherdoost, H. (2023) · Algorithms · doi:10.3390/a16060271 · Cited by 47

Recommending teams promotes prosocial lending in online microfinance

Ai, W., Chen, R., Chen, Y., Mei, Q., & Phillips, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1606085113 · Cited by 43

Predicting food crises using news streams

Balashankar, A., Subramanian, L., & Fraiberger, S. P. (2023) · Science Advances · doi:10.1126/sciadv.abm3449 · Cited by 42

Special Issue Editorial: Addressing Societal Challenges through Analytics: An ESG ICE Framework and Research Agenda

Ketter, W., Padmanabhan, B., Pant, G., & Raghu, T. S. (2020) · Journal of the Association for Information Systems · doi:10.17705/1jais.00631 · Cited by 42

On the Inequity of Predicting A While Hoping for B

Mullainathan, S., & Obermeyer, Z. (2021) · AEA Papers and Proceedings · doi:10.1257/pandp.20211078 · Cited by 42

Ensemble modelling in descriptive epidemiology: burden of disease estimation

Bannick, M. S., McGaughey, M., & Flaxman, A. D. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz223 · Cited by 41

Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques

Rodríguez‐Rodríguez, I., Rodríguez, J., Pardo-Quiles, D., Heras-González, P., & Chatzigiannakis, I. (2020) · Applied Sciences · doi:10.3390/app10228244 · Cited by 41

Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban LifePreprint

Glaeser, E. L., Kominers, S. D., Luca, M., & Naik, N. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2694723 · Cited by 41

How do judges use large language models? Evidence from Shenzhen

Liu, J. Z., & Li, X. (2024) · The Journal of Legal Analysis · doi:10.1093/jla/laae009 · Cited by 40

Text Selection

Kelly, B., Manela, A., & Moreira, A. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.1947843 · Cited by 40

Predicting Food Crises

Spencer, P. G., Kraay, A., Wang, D., & Andrée, B. P. J. (2020) · World Bank, Washington, DC eBooks · doi:10.1596/1813-9450-9412 · Cited by 40

Towards a design-based approach to accounting research

Leuz, C. (2022) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2022.101550 · Cited by 39

Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines

Lalor, J. P., Abbasi, A., Oketch, K., Yang, Y., & Forsgren, N. (2024) · ACM Transactions on Information Systems · doi:10.1145/3641276 · Cited by 40

Contemporaneous and Post-Program Impacts of a Public Works Program

Bertrand, M., Crépon, B., Marguerie, A. C., & Prémand, P. (2017) · World Bank, Washington, DC eBooks · doi:10.1596/28460 · Cited by 38

For Better or Worse? Revenue Forecasting with Machine Learning Approaches

Chung, I. H., Williams, D. W., & Rok, M. (2022) · Public Performance & Management Review · doi:10.1080/15309576.2022.2073551 · Cited by 37

Large Language Models: An Applied Econometric Framework

Ludwig, J., Mullainathan, S., & Rambachan, A. (2026) · Annual Review of Economics · doi:10.1146/annurev-economics-120925-105620 · Cited by 37

Simplicity Creates Inequity

Kleinberg, J. M., & Mullainathan, S. (2019) · ACM Conference on Economics and Computation (EC) · doi:10.1145/3328526.3329621 · Cited by 37

Efficient and explainable ship selection planning in port state control

Yan, R., Wu, S., Jin, Y. J., Cao, J., & Wang, S. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103924 · Cited by 37

Network analysis of surgical innovation: Measuring value and the virality of diffusion in robotic surgery

Garas, G., Cingolani, I., Panzarasa, P., Darzi, A. W., & Athanasiou, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183332 · Cited by 37

Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal

Athey, S. C., Keleher, N., & Spiess, J. (2025) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105945 · Cited by 36

A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers

Ballestar, M. T., Doncel, L. M., Sáinz, J., & Ortigosa-Blanch, A. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119756 · Cited by 36

Identifying Prediction Mistakes in Observational Data

Rambachan, A. (2024) · The Quarterly Journal of Economics · doi:10.1093/qje/qjae013 · Cited by 36

Social Computing Unhinged

Evans, J. (2020) · Journal of Social Computing · doi:10.23919/jsc.2020.0002 · Cited by 35

Gotham city. Predicting ‘corrupted’ municipalities with machine learning

de Blasio, G., D’Ignazio, A., & Letta, M. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122016 · Cited by 35

AI, Behavioural Science, and Consumer Welfare

Mills, S., Costa, S., & Sunstein, C. R. (2023) · Journal of Consumer Policy · doi:10.1007/s10603-023-09547-6 · Cited by 35

Improving Human Sequential Decision Making with Reinforcement Learning

Bastani, H., Bastani, O., & Sinchaisri, W. P. (2025) · Management Science · doi:10.1287/mnsc.2022.02455 · Cited by 35

Machine learning in accounting and finance research: a literature review

Liaras, E., Nerantzidis, M., & Alexandridis, A. (2024) · Review of Quantitative Finance and Accounting · doi:10.1007/s11156-024-01306-z · Cited by 34

Introduction to the Special Collection on the Fragile Families Challenge

Salganik, M., Lundberg, I., Kindel, A. T., & McLanahan, S. (2019) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023119871580 · Cited by 35

Predicting industry sectors from financial statements: An illustration of machine learning in accounting research

van der Heijden, H. (2022) · The British Accounting Review · doi:10.1016/j.bar.2022.101096 · Cited by 34

Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications

Resce, G., & Vaquero‐Piñeiro, C. (2022) · Food Policy · doi:10.1016/j.foodpol.2022.102345 · Cited by 34

Exploring cross-national divide in government adoption of artificial intelligence: Insights from explainable artificial intelligence techniques

Wang, S., Xiao, Y., & Liang, Z. (2024) · Telematics and Informatics · doi:10.1016/j.tele.2024.102134 · Cited by 33

Satellite data and machine learning tools for predicting poverty in rural India

Subash, S., Kumar, R. R., & Aditya, K. S. (2018) · Agricultural Economics Research Review · doi:10.5958/0974-0279.2018.00040.x · Cited by 33

Good value for public money? The case of R&D policy

Aiello, F., Albanese, G., & Piselli, P. (2019) · Journal of Policy Modeling · doi:10.1016/j.jpolmod.2019.02.006 · Cited by 33

A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections

Mejia, J., Mankad, S., & Gopal, A. (2019) · Information Systems Research · doi:10.1287/isre.2019.0866 · Cited by 33

Doing Data Science: A Framework and Case Study

Keller‐McNulty, S., Shipp, S. S., Schroeder, A. D., & Korkmaz, G. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.2d83f7f5 · Cited by 33

Big Data in economics

Harding, M., & Hersh, J. S. (2018) · IZA World of Labor · doi:10.15185/izawol.451 · Cited by 32

Transparency, Reproducibility, and the Credibility of Economics Research

Christensen, G., & Miguel, E. A. (2016) · National Bureau of Economic Research · doi:10.3386/w22989 · Cited by 32

Machine learning in international business

Bosma, B., & van Witteloostuijn, A. C. (2024) · Journal of International Business Studies · doi:10.1057/s41267-024-00687-6 · Cited by 31

Statistical Modeling: The Three Cultures

Daoud, A., & Dubhashi, D. (2023) · Harvard Data Science Review · doi:10.1162/99608f92.89f6fe66 · Cited by 31

Ethics in research

Correia, M. I. T. D. (2023) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2022.12.010 · Cited by 31

Privacy, Ethics, and Data Access: A Case Study of the Fragile Families Challenge

Lundberg, I., Narayanan, A., Levy, K. E. C., & Salganik, M. J. (2019) · Socius Sociological Research for a Dynamic World · doi:10.1177/2378023118813023 · Cited by 31

The Flaws of Policies Requiring Human Oversight of Government Algorithms

Green, B. (2021) · doi:10.2139/ssrn.3921216 · Cited by 259

Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia—is preventive and personalized approach on the horizon?

Berger, J. S., Haskell, L. P., Ting, W., Lurie, F., Chang, S., Mueller, L. A., Elder, K., Rich, K., Crivera, C., Schein, J. R., et al. (2020) · The EPMA Journal · doi:10.1007/s13167-019-00196-9 · Cited by 30

Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels

Silveira, D., Vasconcelos, S. P., Resende, M., & Cajueiro, D. O. (2021) · Energy Economics · doi:10.1016/j.eneco.2021.105711 · Cited by 30

Motivating Experts to Contribute to Digital Public Goods: A Personalized Field Experiment on Wikipedia

Chen, Y., Farzan, R., Kraut, R. E., YeckehZaare, I., & Zhang, A. F. (2020) · Management Science · doi:10.1287/mnsc.2023.4852 · Cited by 30

Pre-registration and Results-Free Review in Observational and Qualitative Research

Jacobs, A. M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108762519.009 · Cited by 30

Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example

Lock, O., Bain, M., & Pettit, C. J. (2020) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/2399808320939974 · Cited by 30

Using digital technologies to improve the design and enforcement of public policies

OECD (2019) · OECD digital economy papers · doi:10.1787/99b9ba70-en · Cited by 30

Untitled work

(2020) · doi:10.1145/3351095.3375784 · Cited by 29

Predicting preventable hospital readmissions with causal machine learning

Marafino, B. J., Schuler, A., Liu, V. X., Escobar, G. J., & Baiocchi, M. (2020) · Health Services Research · doi:10.1111/1475-6773.13586 · Cited by 29

Predicting winners and losers under time-of-use tariffs using smart meter data

Kiguchi, Y., Weeks, M., & Arakawa, R. (2021) · Energy · doi:10.1016/j.energy.2021.121438 · Cited by 29

School dropout prediction and feature importance exploration in Malawi using household panel data: machine learning approach

Öz, H. Ç., Güven, Ç., & Nápoles, G. (2022) · Journal of Computational Social Science · doi:10.1007/s42001-022-00195-3 · Cited by 29

Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

Augenblick, N., Kolstad, J., Obermeyer, Z., & Wang, A. (2020) · National Bureau of Economic Research · doi:10.3386/w27457 · Cited by 29

Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making

Guerdan, L., Coston, A., Wu, Z. S., & Holstein, K. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594036 · Cited by 28

Apparent Algorithmic Bias and Algorithmic Learning

Lambrecht, A., & Tucker, C. E. (2020) · Quantitative Marketing and Economics · doi:10.1007/s11129-024-09286-z · Cited by 28

Supervised Learning for the Prediction of Firm Dynamics

Bargagli-Stoffi, F. J., Niederreiter, J., & Riccaboni, M. (2021) · Springer eBooks · doi:10.1007/978-3-030-66891-4_2 · Cited by 28

Economic development, weather shocks and child marriage in South Asia: A machine learning approach

Dietrich, S., Meysonnat, A., Rosales, F., Cebotari, V., & Gassmann, F. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271373 · Cited by 27

Forecasting Inflation in Russia Using Neural Networks

Pavlov, E. (2020) · Russian Journal of Money and Finance · doi:10.31477/rjmf.202001.57 · Cited by 26

Hospital productivity: The role of efficiency drivers

Ali, M., Salehnejad, R., & Mansur, M. (2019) · The International Journal of Health Planning and Management · doi:10.1002/hpm.2739 · Cited by 26

Machine learning and the optimization of prediction-based policies

Battiston, P., Gamba, S., & Santoro, A. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.123080 · Cited by 26

Driving forces of digital transformation in chinese enterprises based on machine learning

Chen, Q.-A., Xu, Z., Zhang, X., Jiang, Z., & Wang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-56448-w · Cited by 26

Algorithmes de prix, intelligence artificielle et équilibres collusifs

Marty, F. M. (2017) · Revue internationale de droit économique · doi:10.3917/ride.312.0083 · Cited by 26

Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care

Mullainathan, S., & Obermeyer, Z. (2019) · National Bureau of Economic Research · doi:10.3386/w26168 · Cited by 26

On the (Mis)Use of Machine Learning With Panel Data

Cerqua, A., Letta, M., & Pinto, G. (2025) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.70019 · Cited by 25

Predicting Fiscal Crises: A Machine Learning Approach

Hellwig, K.-P. (2021) · IMF Working Paper · doi:10.5089/9781513573588.001 · Cited by 25

Prediction model for myocardial injury after non-cardiac surgery using machine learning

Oh, A. R., Park, J., Shin, S. J., Choi, B., Lee, J., Lee, S. H., & Yang, K. (2023) · Scientific Reports · doi:10.1038/s41598-022-26617-w · Cited by 25

Learning When to Advise Human Decision Makers

Noti, G., & Chen, Y. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/339 · Cited by 25

Validation Methods to Promote Real-world Applicability of Machine Learning in Medicine

Rafiq, R. B., Modave, F. P., Guha, S., & Albert, M. V. (2020) · International Conference on Digital Medicine and Image Processing (DMIP) · doi:10.1145/3441369.3441372 · Cited by 25

Machine-Learning Tests for Effects on Multiple OutcomesPreprint

Ludwig, J., Mullainathan, S., & Spiess, J. (2017) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1707.01473 · Cited by 26

Targeting Policy-Compliers with Machine Learning: An Application to a Tax Rebate Programme in ItalyPreprint

Andini, M., Ciani, ‪., de Blasio, G., D’Ignazio, A., & Salvestrini, V. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3084031 · Cited by 25

Predictive analytics approach to evaluate wage inequality in engineering organizations

Jafari, A., Rouhanizadeh, B., Kermanshachi, S., & Murrieum, M. (2020) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000841 · Cited by 24

Chatbot applications in government frontline services: leveraging artificial intelligence and data governance to reduce problems and increase effectiveness

Wang, C.-W., Hsu, B.-Y., & Chen, D. (2024) · Asia Pacific Journal of Public Administration · doi:10.1080/23276665.2024.2397677 · Cited by 24

The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach

Cheng, L. T. W., Cheong, T. S., Wojewodzki, M., & Chui, D. K. H. (2024) · Research in International Business and Finance · doi:10.1016/j.ribaf.2024.102616 · Cited by 24

Quantifying the Causal Effects of Conversational Tendencies

Zhang, J., Mullainathan, S., & Danescu-Niculescu-Mizil, C. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415202 · Cited by 24

The origins of unpredictability in life outcome prediction tasks

Lundberg, I., Brown‐Weinstock, R., Clampet‐Lundquist, S., Pachman, S., Nelson, T. J., Yang, V., Edin, K. J., & Salganik, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2322973121 · Cited by 25

The Impacts of Machine Learning in Financial Crisis Prediction

Xandr, & Vadlamudi, S. (2020) · Asian Business Review · doi:10.18034/abr.v10i3.528 · Cited by 24

Lookahead Bias in Pretrained Language Models

Sarkar, S., & Vafa, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4754678 · Cited by 23

Harnessing Artificial Intelligence

(2024) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/979-8-3693-7190-9.ch003 · Cited by 23

Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting

Albuquerque, P. H. M., Peng, Y., & da Silva, J. P. F. (2022) · Journal of Forecasting · doi:10.1002/for.2894 · Cited by 23

Who are you? Cartel detection using unlabeled data

Silveira, D., de Moraes, L. B., Fiuza, E. P. S., & Cajueiro, D. O. (2023) · International Journal of Industrial Organization · doi:10.1016/j.ijindorg.2023.102931 · Cited by 23

Targeting Sustainable Transportation Development: The Support Vector Machine and the Bayesian Optimization Algorithm for Classifying Household Vehicle Ownership

Xu, Z., Aghaabbasi, M., Ali, M., & Macioszek, E. (2022) · Sustainability · doi:10.3390/su141711094 · Cited by 22

Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care

Mullainathan, S., & Obermeyer, Z. (2021) · The Quarterly Journal of Economics · doi:10.1093/qje/qjab046 · Cited by 21

Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data

Xu, C., Subbiah, I. M., Lu, S.-C., Pfob, A., & Sidey-Gibbons, C. J. (2022) · Quality of Life Research · doi:10.1007/s11136-022-03284-y · Cited by 21

Fair and Efficient Allocation of Scarce Resources Based on Predicted Outcomes: Implications for Homeless Service Delivery

Kube, A., Das, S., & Fowler, P. J. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12847 · Cited by 21

Using Machine Learning to Analyze Merger Activity

Jiang, T. (2021) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2021.649501 · Cited by 21

A Machine Learning Approach to Analyze and Support Anti-Corruption PolicyPreprint

Ash, E., Galletta, S., & Giommoni, T. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3589545 · Cited by 21

Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints

Watson-Daniels, J., Barocas, S., Hofman, J. M., & Chouldechova, A. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3593998 · Cited by 20

Detecting accounting fraud in family firms: Evidence from machine learning approaches

Rahman, M. J., & Zhu, H. (2023) · Advances in Accounting · doi:10.1016/j.adiac.2023.100722 · Cited by 20

On the Actionability of Outcome Prediction

Liu, L. T., Barocas, S., Kleinberg, J. M., & Levy, K. E. C. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30229 · Cited by 20

Who Shares Fake News? Uncovering Insights from Social Media Users’ Post Histories

Schoenmueller, V., Blanchard, S. J., & Johar, G. V. (2024) · Journal of Marketing Research · doi:10.1177/00222437241281873 · Cited by 20

What failure to predict life outcomes can teach us

Garip, F. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2003390117 · Cited by 20

Identification for Deep Neural Network: Simply Adjusting Few Weights!

Lao, Y., Yang, P., Zhao, W., & Li, P. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00104 · Cited by 20

Artificial Intelligence and the Rule of Law

Huq, A. Z. (2021) · Routledge eBooks · doi:10.4324/9781351237185-22 · Cited by 20

In Search of Information: Use of Google Trends’ Data to Narrow Information Gaps for Low-income Developing Countries

Narita, F., & Yin, R. (2018) · IMF Working Paper · doi:10.5089/9781484390177.001 · Cited by 20

Modelling of Chinese corporate bond default – A machine learning approach

Lu, Z., & Zhuo, Z. (2021) · Accounting and Finance · doi:10.1111/acfi.12846 · Cited by 19

On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement

Wallimann, H., & Sticher, S. (2023) · Transport Policy · doi:10.1016/j.tranpol.2023.09.010 · Cited by 19

Hybrid Predictive Ensembles: Synergies Between Human and Computational Forecasts

Hong, L., Lamberson, P. J., & Page, S. E. (2021) · Journal of Social Computing · doi:10.23919/jsc.2021.0009 · Cited by 19

Do Alliance portfolios encourage or impede new business practice adoption? Theory and evidence from the private equity industry

Sen, P., & Puranam, P. (2022) · Strategic Management Journal · doi:10.1002/smj.3399 · Cited by 18

Can policy achieve the goal of cold chain logistics sustainable development?

Yuan, B., Tao, F., Qin, Y., Chen, Q., & Su, Y. (2025) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2025.104607 · Cited by 18

Criminology in the age of data explosion: New directions

Ozkan, T. (2018) · The Social Science Journal · doi:10.1016/j.soscij.2018.10.010 · Cited by 18

Beyond Prediction: Machine Learning and Causal Inference in Public Policy Research

Tahamont, S. (2017)

Machine Learning and Causal Inference for Policy Evaluation

Athey, S. C. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2785466 · Cited by 101

Machine Learning in Policy Evaluation: New Tools for Causal Inference

Kreif, N., & DiazOrdaz, K. (2019) · Oxford Research Encyclopedia of Economics and Finance · doi:10.1093/acrefore/9780190625979.013.256 · Cited by 57

Applications of Predictive Modeling and Machine Learning to Improve Policy Implementation

Whitaker, S. D. (2017)

Machine Learning and Prediction Errors in Causal InferencePreprint

Allon, G., Chen, D., Jiang, Z., & Zhang, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4480696 · Cited by 6