Chapter 33 References
Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review, 93(1), 113–132.
Abadie, A., & Imbens, G. W. (2002). Simple and bias-corrected matching estimators for average treatment effects. NBER Technical Working Paper No. 283. https://doi.org/10.3386/t0283
Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235–267. https://doi.org/10.1111/j.1468-0262.2006.00655.x
Abadie, A., & Imbens, G. W. (2008). On the failure of the bootstrap for matching estimators. Econometrica, 76(6), 1537–1557. https://doi.org/10.3982/ECTA6474
Abadie, A., & Imbens, G. W. (2011). Bias-corrected matching estimators for average treatment effects. Journal of Business & Economic Statistics, 29(1), 1–11. https://doi.org/10.1198/jbes.2009.07333
Abadie, A., & Imbens, G. W. (2016). Matching on the estimated propensity score. Econometrica, 84, 781–807. https://doi.org/10.3982/ECTA11293
Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505.
Abadie, A., Diamond, A., & Hainmueller, J. (2011). Synth: An R package for synthetic control methods in comparative case studies. Journal of Statistical Software, 42(13), 1–17. https://doi.org/10.18637/jss.v042.i13
Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495–510.
Acemoglu, D., & Finkelstein, A. (2008). Input and technology choices in regulated industries: Evidence from the health care sector. Journal of Political Economy, 116, 837–880.
Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(5), 1369–1401.
Aizer, A., & Doyle, J. J. (2015). Juvenile incarceration, human capital, and future crime: Evidence from randomly assigned judges. Quarterly Journal of Economics, 130(2), 759–803.
Amjad, M., Shah, D., & Shen, D. (2018). Robust synthetic control. Journal of Machine Learning Research, 19(22), 1–50.
Andrews, I., Stock, J. H., & Sun, Y. (2018). Weak instruments in instrumental variables regression: Theory and practice. Annual Review of Economics, 10, 683–706. Retrieved from https://scholar.harvard.edu/files/wirev_092218-_corrected_0.pdf
Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: Evidence from Social Security administrative records. American Economic Review, 80(3), 313–336.
Angrist, J. D., & Krueger, A. B. (1991). Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics, 106(4), 979–1014.
Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.
Arcidiacono, P., & Ellickson, P. B. (2022). Practical Methods for Estimating Dynamic Discrete Choice Models. arXiv:1808.02569.
Arkhangelsky, D., & Imbens, G. (2024). Causal models for longitudinal and panel data: A survey. The Econometrics Journal, 27(3), C1–C61. https://doi.org/10.1093/ectj/utae014
Arkhangelsky, D., Athey, S., Hirshberg, D. A., Imbens, G. W., & Wager, S. (2021). Synthetic difference-in-differences. American Economic Review, 111(12), 4088–4118. https://doi.org/10.1257/aer.20190159
Athey, S., & Imbens, G. W. (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11, 685–725. https://doi.org/10.1146/annurev-economics-080217-053433
Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. The Annals of Statistics, 47(2), 1148–1178.
Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized random forests. Annals of Statistics, 47(2), 1148–1178.
Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine, 28(25), 3083–3107. https://doi.org/10.1002/sim.3697
Austin, P. C., & Small, D. S. (2014). The use of bootstrapping when using propensity-score matching without replacement: A simulation study. Statistics in Medicine, 33(24), 4306–4319.
Autor, D. H., Dorn, D., & Hanson, G. H. (2013). The China syndrome: Local labor market effects of import competition in the United States. American Economic Review, 103(6), 2121–2168.
Banerjee, A., & Duflo, E. (2009). “The Experimental Approach to Development Economics.” Annual Review of Economics, 1, 151–178.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014a). Inference on treatment effects after selection among high-dimensional controls. Review of Economic Studies, 81(2), 608–650.
Belloni, A., Chernozhukov, V., & Hansen, C. (2014b). High-dimensional methods and inference on structural and treatment effects. Journal of Economic Perspectives, 28(2), 29–50.
Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789–1803. https://doi.org/10.1080/01621459.2021.1929245
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(Feb), 281–305.
Berrar, D. (2019). Cross-validation. In Encyclopedia of Bioinformatics and Computational Biology (pp. 542–545). Elsevier.
Berry, S., & Haile, P. (2021). Foundations of Demand Estimation. CFDP 2301, revised May 2022. https://cowles.yale.edu/research/cfdp-2301-foundations-demand-estimation
Bohn, S., Lofstrom, M., & Raphael, S. (2014). Did the 2007 Legal Arizona Workers Act reduce the state’s unauthorized immigrant population? Review of Economics and Statistics, 96(2), 258–269.
Borjas, G. J. (2021). Labor Economics (8th ed.). McGraw-Hill Education.
Boshnjaku, A., Krasniqi, E., & Kamberi, F. (2025). The emerging need to integrate digital health literacy as a course into health-related and care-related profession curricula. Frontiers in Public Health. Retrieved from https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1534139/full
Boulesteix, A.-L., Janitza, S., Kruppa, J., & König, I. R. (2012). Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics. WIREs Data Mining and Knowledge Discovery, 2(6), 493–507.
Box, G. E. P. (1976). “Science and Statistics.” Journal of the American Statistical Association, 71(356), 791–799.
Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726
Busshoff, H., Bodory, H., & Lechner, M. (2022). High-resolution treatment effects estimation: Uncovering effect heterogeneities with the modified causal forest. Entropy, 24(8), 1039. https://doi.org/10.3390/e24081039
Bühlmann, P., & van de Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer.
Callaway, B., & Sant’Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200–230.
Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 82(6), 2295–2326.
Calonico, S., Cattaneo, M. D., & Titiunik, R. (2017). Robust nonparametric confidence intervals for regression-discontinuity designs. Econometrica, 85(1), 229–261.
Calonico, S., Cattaneo, M. D., Farrell, M. H., & Titiunik, R. (2019). Regression discontinuity designs using covariates. Review of Economics and Statistics, 101(3), 442–451.
Calonico, S., Cattaneo, M. D., Farrell, M. H., Palomba, F., & Titiunik, R. (2025). Treatment effect heterogeneity in regression discontinuity designs [Working paper]. arXiv preprint. https://arxiv.org/abs/2503.13696
Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Houghton Mifflin.
Card, D. (1990). The impact of the Mariel Boatlift on the Miami labor market. Industrial and Labor Relations Review, 43(2), 245–257.
Card, D. (1995). Using geographic variation in college proximity to estimate the return to schooling. In L. N. Christofides, E. K. Grant, & R. Swidinsky (Eds.), Aspects of Labour Market Behaviour: Essays in Honour of John Vanderkamp (pp. 201–222). University of Toronto Press.
Cartwright, N. (2007). Hunting Causes and Using Them: Approaches in Philosophy and Economics. Cambridge University Press.
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2006). Intelligible models for healthcare: Predicting pneumonia risk and hospital readmission. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144.
Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2024). A practical introduction to regression discontinuity designs: Extensions. Cambridge Elements: Quantitative and Computational Methods for Social Science. Cambridge University Press.
Cavallo, E., Galiani, S., Noy, I., & Pantano, J. (2013). Catastrophic natural disasters and economic growth. Review of Economics and Statistics, 95(5), 1549–1561.
Chang, N. C. (2020). Double/debiased machine learning for difference-in-differences models. Econometrics Journal, 23, 177–191.
Chen, H., Tian, X., & Yu, Y. (2020). CausalML: Python package for causal inference with machine learning. GitHub Repository. Retrieved from https://github.com/uber/causalml
Chen, X., Chernozhukov, V., Fernández-Val, I., & Kostyshak, S. (2021). Debiased/double machine learning for instrumental variable quantile regressions. arXiv preprint. https://arxiv.org/abs/1909.12592
Chen, X., Christensen, T., & Kankanala, S. (2025). Adaptive estimation and uniform confidence bands for nonparametric structural functions and elasticities. The Review of Economic Studies, 92(1), 162–196. https://doi.org/10.1093/restud/rdae025
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., & Newey, W. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68. https://academic.oup.com/ectj/article/21/1/C1/5056401
Chernozhukov, V., Hansen, C., & Spindler, M. (2015). Post-selection and post-regularization inference in linear models with many controls and instruments. American Economic Review: Papers & Proceedings, 105(5), 486–490.
Chiu, A., Lan, X., Liu, Z., & Xu, Y. (2025). Causal panel analysis under parallel trends: Lessons from a large reanalysis study. American Political Science Review (conditionally accepted). arXiv preprint arXiv:2309.15983. https://doi.org/10.48550/arXiv.2309.15983
Ciccia, D. (2024). A short note on event-study synthetic difference-in-differences estimators. arXiv preprint arXiv:2407.09565. https://doi.org/10.48550/arXiv.2407.09565
Clarke, D., Pailañir, D., Athey, S., & Imbens, G. (2024). On synthetic difference-in-differences and related estimation methods in Stata. The Stata Journal, 24(4), 557–598. https://doi.org/10.1177/1536867X241297914
Cochran, W. G. (1968). The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics, 24(2), 295–313. https://doi.org/10.2307/2528036
Cochran, W. G., & Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhyā: The Indian Journal of Statistics, Series A, 35(4), 417–446. Retrieved from https://www.jstor.org/stable/25049893
Cox, D. R. (2001). Statistical modeling: The two cultures [Comment]. Statistical Science, 16(3), 216–218.
Crudu, P. (2023). Long-term effects of early adverse labour market conditions: A causal machine learning approach. SSRN Working Paper. https://ssrn.com/abstract=4592117
Currie, J., & Gruber, J. (1996). Saving babies: The efficacy and cost of recent changes in the Medicaid eligibility of pregnant women. Journal of Political Economy, 104(6), 1263–1296.
Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792.
Daubechies, I., Defrise, M., & De Mol, C. (2004). An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics, 57(11), 1413–1457.
Davies, N. M., Smith, G. D., & Windmeijer, F. (2023). Reading and conducting instrumental variable studies: A guide for researchers. The BMJ, 387, e078093.
Davis, J. M. V., & Heller, S. B. (2020). Rethinking the benefits of youth employment programs: The heterogeneous effects of summer jobs. The Review of Economics and Statistics, 102(4), 664–677. https://doi.org/10.1162/rest_a_00850
de Chaisemartin, C., & d’Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review, 110(9), 2964–2996.
de Chaisemartin, C., & d’Haultfœuille, X. (2023). Credible answers to hard questions: Differences-in-differences for natural experiments. SSRN Working Paper. https://ssrn.com/abstract=4487202 or https://doi.org/10.2139/ssrn.4487202
de Chaisemartin, C., & D’Haultfœuille, X. (2024). Difference-in-differences estimators of intertemporal treatment effects. The Review of Economics and Statistics. https://doi.org/10.1162/rest_a_01414
Deaton, A., & Cartwright, N. (2018). “Understanding and Misunderstanding Randomized Controlled Trials.” Social Science & Medicine, 210, 2–21.
Diebold, F. X. (2015). Forecasting in Economics, Business, Finance, and Beyond. Princeton University Press.
Dinkelman, T. (2011). The effects of rural electrification on employment: New evidence from South Africa. American Economic Review, 101(7), 3078–3108.
Dobkin, C., Finkelstein, A., Kluender, R., & Notowidigdo, M. J. (2018). The economic consequences of hospital admissions. American Economic Review, 108(2), 308–352.
Dube, A., & Zipperer, B. (2022). Minimum wages and employment: A case study of the fast-food industry. Quarterly Journal of Economics.
Efron, B. (2001). Statistical modeling: The two cultures [Comment]. Statistical Science, 16(3), 218–219.
Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613
Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. Chapman & Hall.
Feng, Z., Lu, Y., & Wang, R. (2022). Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach. Health & Place, 75, 102825. Retrieved from https://www.sciencedirect.com/science/article/pii/S1353829222000053
Freijeiro-González, L., Febrero-Bande, M., & González-Manteiga, W. (2022). A critical review of LASSO and its derivatives for variable selection under dependence among covariates. International Statistical Review, 90(1), 12469. https://doi.org/10.1111/insr.12469
Friedman, J., Hastie, T., & Tibshirani, R. (2007). Pathwise coordinate optimization. The Annals of Applied Statistics, 1(2), 302–332.
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22.
Friedman, M. (1953). “The Methodology of Positive Economics.” In Essays in Positive Economics, University of Chicago Press.
Fu, W. J. (1998). Penalized regressions: The bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3), 397–416.
Fuller, S., & Rametta, J. T. (2024). Causal forest and doubly robust machine learning for political science. OSF.
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011). Doubly robust estimation of causal effects. American Journal of Epidemiology, 173(7), 761–767. https://doi.org/10.1093/aje/kwq439
Gelman, A. (2021). Reflections on Breiman’s two cultures of statistical modeling. Observational Studies, 7(1), 95–98. https://doi.org/10.1353/obs.2021.0025
Gelman, A., & Imbens, G. W. (2019). Why high-order polynomials should not be used in regression discontinuity designs. Journal of Business & Economic Statistics, 37(3), 447–456.
Glynn, A. N., & Quinn, K. M. (2010). An introduction to the augmented inverse propensity weighted estimator. Political Analysis, 18(1), 36–56.
Goldsmith-Pinkham, P., Hull, P., & Kolesár, M. (2020). Assessing the validity of using Bartik instruments for natural experiments. NBER Working Paper No. 24426.
Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254–277.
Grembi, V., Nannicini, T., & Troiano, U. (2016). Do fiscal rules matter? American Economic Journal: Applied Economics, 8(3), 1–30.
Gruber, S., & van der Laan, M. J. (2009). Targeted maximum likelihood estimation: A gentle introduction. U.C. Berkeley Division of Biostatistics Working Paper Series, Working Paper 252. Retrieved from https://biostats.bepress.com/ucbbiostat/paper252
Haddad, M., Huber, M., & Zhang, L. (2024). Difference-in-differences with time-varying continuous treatments using double/debiased machine learning. arXiv preprint arXiv:2410.21105. https://arxiv.org/abs/2410.21105
Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69(1), 201–209.
Handayani, Y. (2025). Implementation of good governance in Indonesia in an effort to minimize corruption crime. Journal of Health, Economics, and Social Sciences. Retrieved from https://www.jurnal.unismuhpalu.ac.id/index.php/IJHESS/article/download/6878/4872
Hansen, B. E. (2022). Econometrics. Princeton University Press
Hartford, J., Lewis, G., Leyton-Brown, K., & Taddy, M. (2017). Deep IV: A flexible approach for counterfactual prediction. Proceedings of the 34th International Conference on Machine Learning (ICML), 1414–1423. Retrieved from http://proceedings.mlr.press/v70/hartford17a.html
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer.
Hettinger, G., Lee, Y., & Mitra, N. (2025). Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures. Biometrics, 81(1), ujaf015. https://doi.org/10.1093/biomtc/ujaf015
Hoerl, A. E., & Kennard, R. W. (1970a). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67. https://doi.org/10.1080/00401706.1970.10488634
Hoerl, A. E., & Kennard, R. W. (1970b). Ridge regression: Applications to nonorthogonal problems. Technometrics, 12(1), 69–82. https://doi.org/10.1080/00401706.1970.10488635
Hsu, Y., & Shen, S. (2024). Dynamic regression discontinuity under treatment effect heterogeneity. Quantitative Economics, 15(4), 1035–1064. https://doi.org/10.3982/QE2150
Huang, J., Ma, S., & Zhang, C. H. (2008). Adaptive lasso for sparse high-dimensional regression models. Statistica Sinica, 18, 1603–1618.
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62(2), 467–475.
Imbens, G. W., & Kalyanaraman, K. (2012). Optimal bandwidth choice for the regression discontinuity estimator. The Review of Economic Studies, 79(3), 933–959.
Imbens, G. W., & Rubin, D. B. (2015). Causal Inference in Statistics, Social, and Biomedical Sciences. Cambridge University Press.
Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86. https://doi.org/10.1257/jel.47.1.5
Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. Journal of Machine Learning Research, 15, 2869–2909.
Jiang, K., Xu, W., & Danowski, J. (2025). Network analysis of social media texts. Frontiers in Research Metrics and Analytics. Retrieved from https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2025.1558798/full
Jin, F. F., Naghi, A. A., & Pick, A. (2019). Heterogeneous treatment effects of educational interventions by using random forests. Erasmus University Thesis.
Kang, H., Guo, Z., Liu, Z., & Small, D. (2024). Identification and inference with invalid instruments. arXiv preprint arXiv:2407.19558. https://arxiv.org/abs/2407.19558
Kennedy, E. H. (2022). Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv preprint arXiv:2004.14497. https://arxiv.org/abs/2004.14497
King, G., & Nielsen, R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27(4), 435–454. https://doi.org/10.1017/pan.2019.11
Klemperer, P., & Meyer, M. (1986). Price competition vs. quantity competition: The role of uncertainty. RAND Journal of Economics, 17(4), 618–638.
Kreif, N., DiazOrdaz, K., & Moreno-Serra, R. (2022). Estimating heterogeneous policy impacts using causal machine learning: A case study of health insurance reform in Indonesia. Health Services and Outcomes Research Methods, 22, 192–227. https://doi.org/10.1007/s10742-021-00259-3
Kreif, N., Grieve, R., Hangartner, D., Turner, A. J., Nikolova, S., & Sutton, M. (2016). Examination of the synthetic control method for evaluating health policies with multiple treated units. Health Economics, 25(12), 1514–1528.
Kreiss, A., & Rothe, C. (2024). Inference in regression discontinuity designs with high-dimensional covariates. The Econometrics Journal, 26(2), 105–123. https://doi.org/10.1093/ectj/utac029
Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.
Kunzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences, 116(10), 4156–4165. https://doi.org/10.1073/pnas.1804597116
Kwak, D., Liang, Y., Shi, X., & Tan, X. (2024). Comparing machine learning and advanced methods with traditional methods to generate weights in inverse probability of treatment weighting: The INFORM study. Pragmatic and Observational Research, 15, 173–183. https://doi.org/10.2147/POR.S466505
Lechner, M. (2002). Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(1), 59–82.
Lechner, M., & Mareckova, J. (2025). Comprehensive causal machine learning. arXiv preprint arXiv:2405.10198. https://doi.org/10.48550/arXiv.2405.10198
Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine, 29(3), 337–346. https://doi.org/10.1002/sim.3782
Lesko, C. R., Henderson, N. C., & Varadhan, R. (2018). Considerations when assessing heterogeneity of treatment effect in patient-centered outcomes research. Journal of Clinical Epidemiology, 100, 22–31. https://doi.org/10.1016/j.jclinepi.2018.04.005
Leuven, E., & Sianesi, B. (2003). psmatch2: Stata module to perform full Mahalanobis and propensity score matching. Statistical Software Components.
Levis, B., Kennedy, C. J., & Keele, L. (2024). Nonparametric identification and efficient estimation of causal effects using instrumental variables. Econometrics Journal, forthcoming.
Lin, W. (2013). Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique. Annals of Applied Statistics, 7, 295–318. Retrieved from https://arxiv.org/pdf/1208.2301
Lin, W., Ding, P., & Han, Z. (2023). Efficiency gains of matching over regression adjustment in causal inference. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2023.2166827
Love, T. E. (2002). Displaying covariate balance: Love plots. Cleveland Clinic Biostatistics Workshop. Retrieved from https://bioinformatics.ccf.org
Lucas, R. E. (1976). Econometric Policy Evaluation: A Critique. In K. Brunner & A. Meltzer (Eds.), The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy.
Lunceford, J. K., & Davidian, M. (2004). Augmented inverse probability weighting and the double robust estimator. Biometrics, 60(2), 353–361.
Ma, X., Karimpour, A., & Wu, Y.-J. (2023). A causal inference approach to eliminate the impacts of interfering factors on traffic performance evaluation. arXiv preprint. https://arxiv.org/abs/2308.03545
Ma, Y., & Wang, L. (2018). Robust inference using inverse probability weighting. arXiv preprint arXiv:1810.11397. Retrieved from https://arxiv.org/abs/1810.11397
Manski, C. F. (2019). Patient Care under Uncertainty. Princeton University Press.
McCrary, J. (2008). Manipulation of the running variable in the regression discontinuity. Journal of Econometrics, 142(2), 698–714.
McLean, C. (2025). The aquanaut: Still a tool for ocean science. Frontiers in Marine Science. Retrieved from https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1527929/full
Miguel, E., Satyanath, S., & Sergenti, E. (2004). Economic shocks and civil conflict: An instrumental variables approach. Journal of Political Economy, 112(4), 725–753.
Mogstad, M., & Torgovitsky, A. (2018). Identification and extrapolation of causal effects with instrumental variables. Annual Review of Economics, 10, 577–613.
Mullainathan, S., & Spiess, J. (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2), 87–106.
Nakamura, E., & Steinsson, J. (2018). Identification in macroeconomics. Journal of Economic Perspectives, 32(3), 59–86.
Nie, X., & Wager, S. (2021). Quasi-oracle estimation of heterogeneous treatment effects. Biometrika, 108(2), 299–319. https://doi.org/10.1093/biomet/asaa076
Noack, C., Olma, T., & Rothe, C. (2024). Flexible covariate adjustments in regression discontinuity designs. arXiv preprint arXiv:2107.07942. https://arxiv.org/abs/2107.07942
Nunn, N., & Qian, N. (2014). U.S. food aid and civil conflict. American Economic Review, 104(6), 1630–1666.
Observational Studies Special Issue: Commentaries on Breiman’s Two Cultures Paper. (2021). Observational Studies, 7(1). https://muse.jhu.edu/issue/45147
Ogburn, E. L., & Shpitser, I. (2021). Causal modelling: The two cultures. Observational Studies, 7(1), 179–183. https://doi.org/10.1353/obs.2021.0006
Piot-Lepetit, I. (2025). Strategies of digitalization and sustainability in agrifood value chains. Frontiers in Sustainable Food Systems. Retrieved from https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1565662/full
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199.
Puterman, E., Weiss, J., Hives, B. A., Gemmill, A., Karasek, D., Mendes, W. B., & Adler, N. (2020). Predicting mortality from 57 economic, behavioral, social, and psychological factors. Proceedings of the National Academy of Sciences, 117(15), 8456–8463. Retrieved from https://www.pnas.org/doi/abs/10.1073/pnas.1918455117
Rahayu, N. S., & Pradita, A. R. (2025). Optimizing the use of domestic products within the Ministry of State Apparatus and Bureaucratic Reforms. International Journal of Health, Economics, and Social Sciences. Retrieved from https://www.jurnal.unismuhpalu.ac.id/index.php/IJHESS/article/download/6383/4836
Rajagopal, D., & Subramanian, P. K. T. (2025). AI augmented edge and fog computing for Internet of Health Things (IoHT). PeerJ Computer Science. Retrieved from https://peerj.com/articles/cs-2431.pdf
Rehill, P. (2025). How do applied researchers use the causal forest? A methodological review. International Statistical Review. https://doi.org/10.1111/insr.12610
Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some predictors are not always observed. Journal of the American Statistical Association, 89(427), 846–866.
Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79(387), 516–524. https://doi.org/10.2307/2288398
Rubin, D. B. (1978). Bayesian inference for causal effects: The role of randomization. Annals of Statistics, 6, 34–58.
Rubin, D. B. (1980). Randomization analysis of experimental data: The Fisher randomization test comment. Journal of the American Statistical Association, 75(371), 591–593.
Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2, 169–188. https://doi.org/10.1023/A:1020363010465
Sadorsky, P. (2021). A random forests approach to predicting clean energy stock prices. Journal of Risk and Financial Management, 14(2), 48. Retrieved from https://www.mdpi.com/1911-8074/14/2/48
Salditt, M., Eckes, T., & Nestler, S. (2024). A tutorial introduction to heterogeneous treatment effect estimation with meta-learners. Administration and Policy in Mental Health and Mental Health Services Research, 51, 650–673. https://doi.org/10.1007/s10488-023-01303-9
Schultz Lindenmeyer, G., & da Silva Torrent, H. (2024). Boosting and predictability of macroeconomic variables: Evidence from Brazil. Computational Economics, 64, 377–409. https://doi.org/10.1007/s10614-023-10421-3
Shah, V., Kreif, N., & Jones, A. M. (2023). Evaluating the heterogeneous impacts of Indonesia’s national health insurance scheme using causal machine learning. In N. Hashimzade & M. Thornton (Eds.), Handbook of Research Methods and Applications in Empirical Microeconomics. Edward Elgar Publishing.
Shao, D., Soleymani, A., Quinzan, F., & Kwiatkowska, M. (2024). Learning decision policies with instrumental variables through double machine learning. arXiv preprint. https://arxiv.org/abs/2405.08498
Sharma, S., & Bangur, P. (2025). Circling to wellness: Health implications of transitioning to a circular economy. Circular Economy and Sustainability. Retrieved from https://link.springer.com/article/10.1007/s43615-025-00507-5
Shirvaikar, V., Lin, X., & Holmes, C. (2023). Targeting relative risk heterogeneity with causal forests. arXiv preprint. https://arxiv.org/abs/2309.15793
Shmueli, G. (2010). To Explain or To Predict? Statistical Science, 25(3), 289–310. https://dx.doi.org/10.2139/ssrn.1351252
Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York: Oxford Academic. Online edition published September 1, 2009.
Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586. Retrieved from https://www.nber.org/system/files/working_papers/t0284/t0284.pdf
Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (pp. 80–108). Cambridge University Press. Retrieved from https://scholar.harvard.edu/files/stock/files/testing_for_weak_instruments_in_linear_iv_regression.pdf
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1–21. https://doi.org/10.1214/09-STS313
Sverdrup, E., Petukhova, M., & Wager, S. (2024). Estimating treatment effect heterogeneity in psychiatry: A review and tutorial with causal forests. arXiv preprint. https://arxiv.org/abs/2409.01578
Słoczyński, T., Uysal, S. D., & Wooldridge, J. M. (2023). Covariate balancing and the equivalence of weighting and doubly robust estimators of average treatment effects. arXiv preprint arXiv:2310.18563. Retrieved from https://arxiv.org/abs/2310.18563
Tamba, W. P., Yanti, F., & Tamba, D. (2025). Jakarta waste management policy and the capacity crisis of Bantargebang TPST: An environmental justice review. Renai Journal. Retrieved from https://renai-journal.percik.or.id/index.php/renai/article/download/15/12
Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.
Thoplan, R. (2014). Random forests for poverty classification. International Journal of Sciences: Basic and Applied. Retrieved from https://www.researchgate.net/profile/Ruben-Thoplan/publication/264785074_Random_Forests_for_Poverty_Classification/links/53ef8d0a0cf2711e0c42f4b4/Random-Forests-for-Poverty-Classification.pdf
Tibshirani, R. J. (2013). The lasso problem and uniqueness. arXiv preprint, arXiv:1206.0313. https://doi.org/10.48550/arXiv.1206.0313
van de Geer, S., Bühlmann, P., & Zhou, S. (2011). The adaptive and the thresholded lasso for potentially misspecified models (and a lower bound for the lasso). Electronic Journal of Statistics, 5, 688–749. https://doi.org/10.1214/11-EJS624
van de Geer, S., Bühlmann, P., Ritov, Y., & Dezeure, R. (2014). On asymptotically optimal confidence regions and tests for high-dimensional models. Annals of Statistics, 42(3), 1166–1202. https://doi.org/10.1214/14-AOS1221
Varadhan, R., & Seeger, J. D. (2013). Estimation and reporting of heterogeneity of treatment effects. In P. Velentgas, N. A. Dreyer, P. Nourjah, et al. (Eds.), Developing a Protocol for Observational Comparative Effectiveness Research: A User’s Guide (Chapter 3). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK126188/
Wager, S., & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association, 113(523), 1228–1242. arXiv:1712.09988.
Wang, C., Wang, S., Shi, F., & Wang, Z. (2018). Robust propensity score computation method based on machine learning with label-corrupted data. arXiv preprint. https://arxiv.org/abs/1801.03132
Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press.
Xu, Y. (2017). Generalized synthetic control method. Political Analysis, 25(1), 57–76.
Xu, Y., & Liu, L. (2022). gsynth: Generalized Synthetic Control Method. R package version 1.2.1. https://yiqingxu.org/packages/gsynth/
Yadlowsky, S., Fleming, S., Shah, N., Brunskill, E., & Wager, S. (2021). Evaluating treatment prioritization rules via rank-weighted average treatment effects. arXiv preprint arXiv:2111.07966. https://arxiv.org/abs/2111.07966
Young, A. (2022). Channeling Fisher: Randomization tests and the statistical insignificance of seemingly significant experimental results. Quarterly Journal of Economics, 137(2), 611–661.
Yıldız, A. Y., & Kalayci, A. (2025). Gradient boosting decision trees on medical diagnosis over tabular data. arXiv preprint. https://doi.org/10.48550/arXiv.2410.03705
Zhang, C.-H., & Zhang, S. S. (2014). Confidence intervals for low-dimensional parameters in high-dimensional linear models. Journal of the Royal Statistical Society: Series B, 76(1), 217–242.
Zhang, L. Z. (2025). Continuous difference-in-differences with double/debiased machine learning. arXiv preprint arXiv:2408.10509. https://arxiv.org/abs/2408.10509
Zhang, Y., Chen, S., & Liu, D. (2024). A measurement study of the environmental quality and medical expenditures of elderly individuals: Causal inference based on machine learning. Archives of Public Health, 82, 195. https://doi.org/10.1186/s13690-024-01386-2
Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429.
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. https://doi.org/10.1111/j.1467-9868.2005.00503.x