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   RePEc:egu:wpaper:2512.

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1. Navigating the Measurement Frontier: New Insights Into Small Farm Realities. (2025). Michelson, Hope.
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   RePEc:bla:agecon:v:56:y:2025:i:3:p:526-542.

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2. Leveraging the digital layer: the strength of weak and strong ties in bridging geographic and cognitive distances. (2024). Kruger, Miriam ; Abbasiharofteh, Milad ; Kinne, Jan.
   In: Journal of Economic Geography.
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3. Matrix completion of world trade: An analysis of interpretability through Shapley values. (2023). Riccaboni, Massimo ; Nutarelli, Federico ; Gnecco, Giorgio.
   In: The World Economy.
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4. Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments. (2023). Lee, Ying-Ying ; Colangelo, Kyle.
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5. Estimating Inequality with Missing Incomes. (2022). Verme, Paolo ; SALAS ROJO, PEDRO ; Salas-Rojo, Pedro ; Brunori, Paolo.
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6. Double Machine Learning based Program Evaluation under Unconfoundedness. (2022). Knaus, Michael.
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7. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2022). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
   In: Papers.
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8. The strength of weak and strong ties in bridging geographic and cognitive distances. (2021). Abbasiharofteh, Milad ; Kinne, Jan ; Kruger, Miriam.
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9. Big Data in der makroökonomischen Analyse. (2021). Heidland, Tobias ; Hinz, Julian ; Hauber, Philipp ; Funke, Manuel ; Boysen-Hogrefe, Jens ; Bode, Eckhardt ; Beckmann, Joscha ; Ademmer, Martin ; Soder, Mareike ; Stamer, Vincent ; Stolzenburg, Ulrich ; Kooths, Stefan ; Jannsen, Nils.
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10. Does the accessibility of a farmer predict the delivery of extension services? Evidence from Rwanda. (2021). Kabirigi, Michel.
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11. The Effect of Sport in Online Dating: Evidence from Causal Machine Learning. (2021). Lechner, Michael ; Okasa, Gabriel ; Boller, Daniel.
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12. The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies. (2021). Baiardi, Anna ; Naghi, Andrea A.
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13. Machine Learning and Central Banks: Ready for Prime Time?. (2021). Karagedikli, Ozer ; Genberg, Hans.
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14. Technical efficiency and the adoption of multiple agricultural technologies in the mountainous areas of Northern Vietnam. (2021). To-The, Nguyen ; Nguyen-Anh, Tuan ; Lampach, Nicolas.
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15. LASSO+DEA for small and big wide data. (2021). Zelenyuk, Valentin ; Tsionas, Mike ; Chen, Ya.
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16. Preventing rather than punishing: An early warning model of malfeasance in public procurement. (2021). Gallego, Jorge ; Martinez, Juan ; Rivero, Gonzalo.
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17. Overlap in observational studies with high-dimensional covariates. (2021). Ding, Peng ; Lei, Lihua ; Sekhon, Jasjeet ; Feller, Avi ; Damour, Alexander.
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18. Optimal Targeting in Fundraising: A Causal Machine-Learning Approach. (2021). Strittmatter, Anthony ; Glogowsky, Ulrich ; Cagala, Tobias ; Rincke, Johannes.
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19. Confronting Machine Learning With Financial Research. (2021). el Harzli, Ouns ; Lommers, Kristof ; Kim, Jack.
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20. The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies. (2021). Baiardi, Anna ; Naghi, Andrea A.
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21. Using Machine Learning to Create an Early Warning System for Welfare Recipients. (2021). Sansone, Dario ; Zhu, Anna.
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22. Bottom incomes and the measurement of poverty and inequality. (2020). Verme, Paolo ; Hlasny, Vladimir ; Ceriani, Lidia.
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23. Which Model for Poverty Predictions?. (2020). Verme, Paolo.
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24. Endogenous effects and cluster transition: a conceptual framework for cluster policy. (2020). Abbasiharofteh, Milad.
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25. Double Machine Learning based Program Evaluation under Unconfoundedness. (2020). Knaus, Michael.
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26. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: Economics Working Paper Series.
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27. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: ROA Research Memorandum.
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28. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: Research Memorandum.
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29. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium.
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30. LASSO DEA for small and big data. (2020). Zelenyuk, Valentin ; Tsionas, Mike ; Chen, Ya.
    In: CEPA Working Papers Series.
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31. Long-Term Health Insurance: Theory Meets Evidence. (2020). Ziebarth, Nicolas ; Karlsson, Martin ; Fang, Hanming ; Atal, Juan Pablo.
    In: PIER Working Paper Archive.
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32. What Can Economics Say About Alzheimers Disease?. (2020). Mommaerts, Corina ; Coile, Courtney ; Chandra, Amitabh.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:27760.

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33. Machine Learning in Gravity Models: An Application to Agricultural Trade. (2020). Gopinath, Munisamy ; Beckman, Jayson ; Batarseh, Feras A.
    In: NBER Working Papers.
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34. German Long-Term Health Insurance: Theory Meets Evidence. (2020). Ziebarth, Nicolas ; Karlsson, Martin ; Fang, Hanming ; Atal, Juan Pablo.
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35. Bottom Incomes and the Measurement of Poverty and Inequality. (2020). Verme, Paolo ; Hlasny, Vladimir ; Ceriani, Lidia.
    In: LIS Working papers.
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36. Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions. (2020). Wochner, Daniel.
    In: KOF Working papers.
    RePEc:kof:wpskof:20-472.

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37. What Is at Stake without High-Stakes Exams? Students Evaluation and Admission to College at the Time of COVID-19. (2020). Loviglio, Annalisa ; Calsamiglia, Caterina ; Arenas, Andreu.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp13838.

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38. Double Machine Learning Based Program Evaluation under Unconfoundedness. (2020). Knaus, Michael.
    In: IZA Discussion Papers.
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39. Indicators of Economic Crises: A Data-Driven Clustering Approach. (2020). Araújo, Tanya ; Araujo, Tanya ; Gobel, Maximilian.
    In: Working Papers REM.
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40. Bottom incomes and the measurement of poverty and inequality. (2020). Verme, Paolo ; Hlasny, Vladimir ; Ceriani, Lidia.
    In: Working Papers.
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41. Which Model for Poverty Predictions?. (2020). Verme, Paolo.
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42. Does the estimation of the propensity score by machine learning improve matching estimation? : The case of Germanys programmes for long term unemployed. (2020). Lechner, Michael ; Goller, Daniel ; Wolff, Joachim ; Moczall, Andreas.
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43. Good identification, meet good data. (2020). Zinman, Jonathan ; Udry, Christopher ; Karlan, Dean ; Dillon, Andrew.
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44. Forecasting Costa Rican inflation with machine learning methods. (2020). Rodriguez Vargas, Adolfo ; Rodriguez-Vargas, Adolfo.
    In: Latin American Journal of Central Banking (previously Monetaria).
    RePEc:eee:lajcba:v:1:y:2020:i:1:s2666143820300120.

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45. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: LIDAM Discussion Papers IRES.
    RePEc:ctl:louvir:2020016.

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46. Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Boolens, Joost.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:14270.

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47. Priority of Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium. (2020). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8297.

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48. Supervised learning for the prediction of firm dynamics. (2020). Riccaboni, Massimo ; Niederreiter, Jan ; Bargagli-Stoffi, Falco J.
    In: Papers.
    RePEc:arx:papers:2009.06413.

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49. A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics. (2020). Klose, Sophie-Charlotte ; Lederer, Johannes.
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    RePEc:arx:papers:2006.12296.

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50. Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment. (2020). Tang, Liyang.
    In: Papers.
    RePEc:arx:papers:2005.08735.

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51. Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed. (2019). Lechner, Michael ; Goller, Daniel ; Wolff, Joachim ; Moczall, Andreas.
    In: Economics Working Paper Series.
    RePEc:usg:econwp:2019:10.

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52. Priority to Unemployed Immigrants? A Causal Machine Learning Evaluation of Training in Belgium. (2019). Lechner, Michael ; Cockx, Bart ; Bollens, Joost.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp12875.

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53. Does the Estimation of the Propensity Score by Machine Learning Improve Matching Estimation? The Case of Germanys Programmes for Long Term Unemployed. (2019). Lechner, Michael ; Goller, Daniel ; Wolff, Joachim ; Moczall, Andreas.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp12526.

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54. Double debiased machine learning nonparametric inference with continuous treatments. (2019). Lee, Ying-Ying ; Colangelo, Kyle.
    In: CeMMAP working papers.
    RePEc:ifs:cemmap:54/19.

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