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References for Machine learning for causal inference in economics
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Further reading
- Chernozhukov, V., Hansen, C., Kallus, N., Spindler, M., and Syrgkanis, V. Applied Causal Inference Powered by ML and AI, 2024.
- Athey, S., and Imbens, G. W. "Machine Learning Methods Economists Should Know About." Annual Review of Economics 11 (2024): 685-725.
- James, G., Witten, D., Hastie, T., and Tibshirani, R. An Introduction to Statistical Learning (2nd ed.) Springer, 2023.
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Key references
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Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C. "Sparse models and methods for optimal instruments with an application to eminent domain." Econometrica 80:6 (2012): 2369- 2429. Key reference: [1]
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Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. "Double/debiased machine learning for treatment and structural parameters." Econometrics Journal 21:1 (2018): C1-C68. Key reference: [2]
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Acemoglu, D., Johnson, S., and Robinson, J. A. "The colonial origins of comparative development: An empirical investigation." American Economic Review 91:5 (2001): 1369-1401. Key reference: [3]
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Belloni, A., Chernozhukov, V., and Hansen, C. "High-dimensional methods and inference on structural and treatment effects." Journal of Economic Perspectives 28:2 (2014): 29-50. Key reference: [4]
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Knaus, M. C., Lechner, M., and Strittmatter, A. "Heterogeneous employment effects of job search programs: A machine learning approach." Journal of Human Resources 57:2 (2022): 597- 636. Key reference: [5]
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Wager, S., and Athey, S. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association 113 (2018): 1228-1242. Key reference: [6]
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Bitler, M., Gelbach, J. B., and Hoynes, H. W. "Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity? Evidence from a social experiment." Review of Economics and Statistics 99:4 (2017): 683–697. Key reference: [7]
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Strittmatter, A. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?" Labour Economics 84 (2023): 102412. Key reference: [8]
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Haushofer, J., Niehaus, P., Paramo, C., Miguel, E., and Walker, M. W. Targeting impact versus deprivation. NBER Working Paper 30138, 2022. Key reference: [9]
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Mullainathan, S., and Spiess, J. "Machine learning: An applied econometric approach." Journal of Economic Perspectives 31:2 (2017): 87-106. Key reference: [10]
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Hünermund, P., Louw, B., and Caspi, I. "Double machine learning and automated confounder selection - A cautionary tale." Journal of Causal Inference 11:1 (2023): 20220078. Key reference: [11]
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Blau, F. D., and Kahn, L. M. "The gender wage gap: Extent, trends, and explanations." Journal of Economic Literature 55:3 (2017): 789–865. Key reference: [12]
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Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C. "Sparse models and methods for optimal instruments with an application to eminent domain." Econometrica 80:6 (2012): 2369- 2429.
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Additional References
- Angrist, J. D., and Frandsen, B. "Machine Labor." Journal of Labor Economics 40:S1 (2022): S97-S140.
- Ascarza, E. "Retention futility: targeting high risk customers might be ineffective." Journal of Marketing Research 55:1 (2018): 80-98.
- Ash, E., Galletta, S., and Giommoni, T. A Machine Learning Approach to Analyze and Support Anti-Corruption Policy. CESifo Working Paper No. 9015, 2021.
- Athey, S. "The impact of machine learning on economics." In: Agrawal, A. K., Gans, J. and Goldfarb, A. (eds). The Economics of Artificial Intelligence: An Agenda. Chicago, IL: University of Chicago Press, 2019.
- Athey, S., and Imbens, G. W. "Recursive partitioning for heterogeneous causal effects." Proceedings of the National Academy of Sciences 113:37 (2016): 7353-7360.
- Athey, S., and Imbens, G. W. "The state of applied econometrics: causality and policy evaluation." Journal of Economic Perspectives 31:2 (2017): 3-32.
- Athey, S., Tibshirani, J., and Wager, S. "Generalized random forests." Annals of Statistics 47 (2019): 1148-1178.
- Bach, P., Chernozhukov, V., and Spindler, M. "Heterogeneity in the US gender wage gap." Journal of the Royal Statistical Society Series A 187:1 (2024): 209- 230.
- Baiardi, A., and Naghi, A. A. "The value added of machine learning to causal inference: Evidence from revisited studies." Econometrics Journal 27 (2024): 213-234.
- Belloni, A., Chernozhukov, V., and Hansen, C. "Inference on treatment effects after selection among high-dimensional controls." Review of Economic Studies 81:2 (2014): 608-650.
- Belloni, A., Chernozhukov, V., Fernández-Val, I., and Hansen, C. "Program evaluation and causal inference with high-dimensional data." Econometrica 85:1 (2017): 233-298.
- Bertrand, M., Crépon, B., Marguerie, A., and Premand, P. Do Workfare Programs Live Up to Their Promises? Experimental Evidence from Côte D’Ivoire. NBER Working Paper No. 28664, 2021.
- Cagala, T., Glogowsky, U., Rincke, J., and Strittmatter, A. Optimal targeting in fundraising: A machine learning approach. CESifo Working Paper No. 9037, 2021.
- Cockx, B., Lechner, M., and Bollens, J. "Priority to Unemployed Immigrants: A Causal Machine Learning Evaluation of Training in Belgium." Labour Economics 80 (2023): 102306.
- Fan, Q., Hsu, Y.-C., Lieli, R., and Zhang, Y. "Estimation of Conditional Average Treatment Effects with High Dimensional Data." Journal of Business and Economic Statistics 40:1 (2022): 313-327.
- Farbmacher, H., Huber, M., Laffers, L., Langen, H., and Spindler, M. "Causal mediation analysis with double machine learning." Econometrics Journal 25:2 (2022): 277-300.
- Goller, D., Lechner, M., Moczall, A., and Wolff, J. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed." Labour Economics 65 (2020): 101855.
- Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2009.
- Heiler, P., and Knaus, M. C. (2023). Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments. IZA Discussion Papers 15580.
- Hsu, Y.-C., Lieli, R., and Reguly, A. "The Use of Machine Learning in Treatment Effect Estimation." In: Chan, F. and Matyas, L. (eds). Econometrics with Machine Learning. Advanced Studies in Theoretical and Applied Econometrics, vol 53. Springer, Cham, 2022.
- Klaaßen, S., Kueck, J., and Spindler, M. "Transformation Models in High Dimensions." Journal of Business and Economic Statistics 40:3 (2022): 1168-1178.
- Knaus, M. C. "Double Machine Learning based Program Evaluation under Unconfoundedness." Econometrics Journal 25:3 (2022): 602-627.
- Knaus, M. C., Lechner, M., and Strittmatter, A. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence." Econometrics Journal 25:1 (2021): 29-66.
- Lechner, M. "Causal Machine Learning and its use for public policy." Swiss Journal of Economics and Statistics 159 (2023):8
- Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., and Tibshirani, R. "Some methods for heterogeneous treatment effect estimation in high dimensions." Statistics in Medicine 37:11 (2018): 1767-1787.
- Semenova, V., and Chernozhukov, V. Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions. The Institute for Fiscal Studies, cemmap working paper CWP40/18, 2018.