The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
Recursive partitioning for heterogeneous causal effects
Open accessAbstract
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In particular, Athey and Imbens (2016), Wager and Athey (2018), and Athey et al. (2019) developed random forests methods designed to capture honest heterogeneity in treatment effects, circumventing overfitting issues inherent to traditional methods and thus avoiding mistaking statistical noise for heterogeneity.”
From Prison Rehabilitation Programs: Efficiency and Targeting (2021) · cites this paper as Athey and Imbens (2016)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
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
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,916
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Research works printed but not linked: 1
- J Sigovitch Identifying informative biological markers in high-dimensional genomic data and clinical trials. PhD thesis (Harvard Univ Cambridge MA). (2007).
Cited by
Sorted by citations
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
Self-citationMachine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
Athey, S. C., Tibshirani, J., & Wager, S. (2019) · The Annals of Statistics · doi:10.1214/18-aos1709 · Cited by 2,108
Self-citationThe State of Applied Econometrics: Causality and Policy Evaluation
Athey, S. C., & Imbens, G. W. (2017) · The Journal of Economic Perspectives · doi:10.1257/jep.31.2.3 · Cited by 1,406
Self-citationGentzkow, M. A., Kelly, B., & Taddy, M. A. (2019) · Journal of Economic Literature · doi:10.1257/jel.20181020 · Cited by 1,353
Machine Learning Methods That Economists Should Know About
Athey, S., & Imbens, G. W. (2019) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053433 · Cited by 1,163
Self-citationMetalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S., Sekhon, J., Bickel, P., & Yu, B. (2017) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1804597116 · Cited by 1,458
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
Machine learning and AI in marketing–Connecting computing power to human insights
Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602
The Econometrics of Randomized Experiments
Athey, S. C., & Imbens, G. W. (2017) · Handbook of economic field experiments · doi:10.1016/bs.hefe.2016.10.003 · Cited by 561
Self-citationEstimating Treatment Effects with Causal Forests: An Application
Athey, S. C., & Wager, S. (2019) · Observational Studies · doi:10.1353/obs.2019.0001 · Cited by 576
Self-citationQuasi-oracle estimation of heterogeneous treatment effects
Nie, X., & Wager, S. (2020) · Biometrika · doi:10.1093/biomet/asaa076 · Cited by 754
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · doi:10.3386/w23180 · Cited by 545
What is Machine Learning? A Primer for the Epidemiologist
Bi, Q., Goodman, K. E., Kaminsky, J., & Lessler, J. (2019) · American Journal of Epidemiology · doi:10.1093/aje/kwz189 · Cited by 579
Estimating individual treatment effect: generalization bounds and algorithmsPreprint
Shalit, U., Johansson, F., & Sontag, D. A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03976 · Cited by 574
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
Kent, D. M., Steyerberg, E. W., & Klaveren, D. V. (2018) · BMJ · doi:10.1136/bmj.k4245 · Cited by 518
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3444944 · Cited by 568
Econometric Methods for Program Evaluation
Abadie, A., & Cattaneo, M. D. (2018) · Annual Review of Economics · doi:10.1146/annurev-economics-080217-053402 · Cited by 458
Policy Learning With Observational Data
Athey, S. C., & Wager, S. (2021) · Econometrica · doi:10.3982/ecta15732 · Cited by 382
Self-citationIntegrating explanation and prediction in computational social science
Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., Margetts, H., Mullainathan, S., Salganik, M. J., Vazire, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03659-0 · Cited by 371
Self-citationWhat Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
Lundberg, I., Johnson, R., & Stewart, B. (2021) · American Sociological Review · doi:10.1177/00031224211004187 · Cited by 422
Causal machine learning for predicting treatment outcomes
Feuerriegel, S., Frauen, D., Melnychuk, V., Schweisthal, J., Heß, K., Curth, A., Bauer, S., Kilbertus, N., Kohane, I. S., & van der Schaar, M. (2024) · Nature Medicine · doi:10.1038/s41591-024-02902-1 · Cited by 353
Machine Learning for Social Science: An Agnostic Approach
Grimmer, J., Roberts, M. E., & Stewart, B. (2021) · Annual Review of Political Science · doi:10.1146/annurev-polisci-053119-015921 · Cited by 362
Data ex Machina: Introduction to Big Data
Lazer, D. M. J., & Radford, J. P. (2017) · Annual Review of Sociology · doi:10.1146/annurev-soc-060116-053457 · Cited by 323
A Review of Generalizability and Transportability
Degtiar, I., & Rose, S. (2022) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042522-103837 · Cited by 362
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
Kohavi, R., Tang, D., & Xu, Y. (2020) · doi:10.1017/9781108653985 · Cited by 297
Machine Learning for Health Services Researchers
Doupé, P., Faghmous, J. H., & Basu, S. (2019) · Value in Health · doi:10.1016/j.jval.2019.02.012 · Cited by 311
Machine Learning for Sociology
Molina, M., & Garip, F. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041106 · Cited by 295
Does Conjoint Analysis Mitigate Social Desirability Bias?
Horiuchi, Y., Markovich, Z. D., & Yamamoto, T. (2021) · Political Analysis · doi:10.1017/pan.2021.30 · Cited by 286
Retention Futility: Targeting High-Risk Customers Might be Ineffective
Ascarza, E. (2018) · Journal of Marketing Research · doi:10.1509/jmr.16.0163 · Cited by 295
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 324
Knowledge Discovery: Methods from data mining and machine learning
Shu, X., & Ye, Y. (2022) · Social Science Research · doi:10.1016/j.ssresearch.2022.102817 · Cited by 354
Does information change attitudes toward immigrants?
Grigorieff, A., Roth, C., & Ubfal, D. (2020) · Demography · doi:10.1007/s13524-020-00882-8 · Cited by 254
Dorie, V., Hill, J., Shalit, U., Scott, M. A., & Cervone, D. (2019) · Statistical Science · doi:10.1214/18-sts667 · Cited by 311
A Penalized Synthetic Control Estimator for Disaggregated Data
Abadie, A., & L’Hour, J. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2021.1971535 · Cited by 245
70 years of machine learning in geoscience in review
Dramsch, J. S. (2020) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 225
Machine Learning in Epidemiology and Health Outcomes Research
Wiemken, T. L., & Kelley, R. R. (2019) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094437 · Cited by 239
70 years of machine learning in geoscience in review
Dramsch, J. S. (2020) · Advances in geophysics · doi:10.1016/bs.agph.2020.08.002 · Cited by 253
Machine learning in agricultural and applied economics
Storm, H., Baylis, K., & Heckelei, T. (2019) · European Review of Agricultural Economics · doi:10.1093/erae/jbz033 · Cited by 240
Fokkema, M., Smits, N., Zeileis, A., Hothorn, T., & Kelderman, H. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0971-x · Cited by 223
Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs
Davis, J. M., & Heller, S. (2017) · American Economic Review · doi:10.1257/aer.p20171000 · Cited by 214
Bica, I., Alaa, A. M., Lambert, C., & van der Schaar, M. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1907 · Cited by 222
Some methods for heterogeneous treatment effect estimation in high dimensions
Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T. J., & Tibshirani, R. J. (2018) · Statistics in Medicine · doi:10.1002/sim.7623 · Cited by 211
Methods and tools for causal discovery and causal inference
Nogueira, A. R., Pugnana, A., Ruggieri, S., Pedreschi, D., & da Gama, J. M. P. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1449 · Cited by 227
Chernozhukov, V., Demirer, M., Duflo, E., & Fernández‐Val, I. (2018) · National Bureau of Economic Research · doi:10.3386/w24678 · Cited by 190
In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions
Ascarza, E., Neslin, S. A., Netzer, O., Anderson, Z., Fader, P. S., Gupta, S., Hardie, B. G. S., Lemmens, A., Libai, B., Neal, D., et al. (2018) · Customer Needs and Solutions · doi:10.1007/s40547-017-0080-0 · Cited by 190
Imbens, G. W. (2020) · Journal of Economic Literature · doi:10.1257/jel.20191597 · Cited by 235
Self-citationTowards optimal doubly robust estimation of heterogeneous causal effects
Kennedy, E. H. (2023) · Electronic Journal of Statistics · doi:10.1214/23-ejs2157 · Cited by 185
Deines, J. M., Guan, K., Lopez, B., Zhou, Q., White, C. S., Wang, S., & Lobell, D. B. (2022) · Global Change Biology · doi:10.1111/gcb.16489 · Cited by 178
COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity
Steinert, J., Sternberg, H., Prince, H., Fasolo, B., Galizzi, M. M., Büthe, T., & Veltri, G. (2022) · Science Advances · doi:10.1126/sciadv.abm9825 · Cited by 170
Debiased machine learning of conditional average treatment effects and other causal functions
Semenova, V., & Chernozhukov, V. (2021) · The Econometrics Journal · doi:10.1093/ectj/utaa027 · Cited by 167
A pantropical assessment of deforestation caused by industrial mining
Giljum, S., Maus, V., Kuschnig, N., Luckeneder, S., Tost, M., Sonter, L. J., & Bebbington, A. J. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2118273119 · Cited by 197
Deep IV:A flexible approach for counterfactual prediction
Hartford, J., Lewis, G., Leyton‐Brown, K., & Taddy, M. A. (2017) · Research Explorer (The University of Manchester) · Cited by 164
Personalized Pricing and Consumer Welfare
Dubé, J., & Misra, S. (2022) · Journal of Political Economy · doi:10.1086/720793 · Cited by 166
Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?
Shrestha, Y. R., He, V. F., Puranam, P., & von Krogh, G. (2020) · Organization Science · doi:10.1287/orsc.2020.1382 · Cited by 171
Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs
Davis, J. M., & Heller, S. (2019) · The Review of Economics and Statistics · doi:10.1162/rest_a_00850 · Cited by 155
Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp
Zhang, M., & Luo, L. (2016) · Management Science · doi:10.1287/mnsc.2022.4359 · Cited by 169
Representation Learning for Treatment Effect Estimation from Observational Data
Yao, L., Li, S., Li, Y., Huai, M., Gao, J., & Zhang, A. (2018) · neural information processing systems · Cited by 147
Chalfin, A. J., Hansen, B., Lerner, J., & Parker, L. (2021) · Journal of Quantitative Criminology · doi:10.1007/s10940-020-09490-6 · Cited by 173
Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence
Knaus, M. C., Lechner, M., & Strittmatter, A. (2020) · Econometrics Journal · doi:10.1093/ectj/utaa014 · Cited by 142
Machine Learning at Central BanksPreprint
Chakraborty, C., & Joseph, A. C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3031796 · Cited by 152
Augmented Inverse Probability Weighting and the Double Robustness Property
Kurz, C. (2021) · Medical Decision Making · doi:10.1177/0272989x211027181 · Cited by 136
Managing Churn to Maximize Profits
Lemmens, A., & Гупта, С. (2020) · Marketing Science · doi:10.1287/mksc.2020.1229 · Cited by 140
Cattle ranchers and deforestation in the Brazilian Amazon: production, location, and policies
Skidmore, M. E., Moffette, F., Rausch, L., Christie, M., Munger, J., & Gibbs, H. K. (2021) · Global Environmental Change · doi:10.1016/j.gloenvcha.2021.102280 · Cited by 132
A Causal Framework for Cross-Cultural Generalizability
Deffner, D., Rohrer, J. M., & McElreath, R. L. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221106366 · Cited by 152
Causal Inference: A Missing Data Perspective
Ding, P., & Li, F. (2018) · Statistical Science · doi:10.1214/18-sts645 · Cited by 133
Baum, A., Scarpa, J. R., Bruzelius, E., Tamler, R., Basu, S., & Faghmous, J. H. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30176-6 · Cited by 125
Negotiating a Better Future: How Interpersonal Skills Facilitate Intergenerational Investment*
Ashraf, N., Bau, N., Low, C., & McGinn, K. L. (2018) · Quarterly Journal of Economics · doi:10.1093/qje/qjz039 · Cited by 132
Generic machine learning inference on heterogenous treatment effects in randomized experiments
Chernozhukov, V., Demirer, M., Duflo, E., & Fernández‐Val, I. (2018) · RePEc: Research Papers in Economics · doi:10.1920/wp.cem.2017.6117 · Cited by 149
Wendling, T., Jung, K., Callahan, A., Schuler, A., Shah, N. H., & Gallego, B. (2018) · Statistics in Medicine · doi:10.1002/sim.7820 · Cited by 111
Goals and Gaps: Educational Careers of Immigrant Children
Carlana, M., Ferrara, E. L., & Pinotti, P. (2022) · Econometrica · doi:10.3982/ecta17458 · Cited by 118
Design and Analysis of Experiments
Mattei, A., Mealli, F., & Nodehi, A. (2021) · Springer eBooks · doi:10.1007/978-3-319-57365-6_40-1 · Cited by 118
The Effect of Job Loss and Unemployment Insurance on Crime in Brazil
Britto, D. G. C., Pinotti, P., & Sampaio, B. R. (2022) · Econometrica · doi:10.3982/ecta18984 · Cited by 110
Double machine learning-based programme evaluation under unconfoundedness
Knaus, M. C. (2022) · Econometrics Journal · doi:10.1093/ectj/utac015 · Cited by 112
Strangers in Hostile Lands: Exposure to Refugees and Right-Wing Support in Germany’s Eastern Regions
Schaub, M., Gereke, J., & Baldassarri, D. (2020) · Comparative Political Studies · doi:10.1177/0010414020957675 · Cited by 109
Recent Developments in Causal Inference and Machine Learning
Brand, J. E., Zhou, X., & Xie, Y. F. (2023) · Annual Review of Sociology · doi:10.1146/annurev-soc-030420-015345 · Cited by 132
Heterogeneity of Treatment Effect
Angus, D. C., & Chang, C. H. (2021) · JAMA · doi:10.1001/jama.2021.20552 · Cited by 116
Friedberg, R., Tibshirani, J., Athey, S. C., & Wager, S. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1831930 · Cited by 112
Self-citationEstimating heterogeneous treatment effects with right-censored data via causal survival forests
Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R. (2023) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1093/jrsssb/qkac001 · Cited by 117
Causal Interaction in Factorial Experiments: Application to Conjoint Analysis
Egami, N., & Imai, K. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2018.1476246 · Cited by 104
The Targeting and Impact of Paycheck Protection Program Loans to Small BusinessesPreprint
Bartik, A. W., Cullen, Z. N., Glaeser, E. L., Luca, M., Stanton, C., & Sunderam, A. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3676759 · Cited by 104
Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data
Zhang, W., Yan, S., Li, J., Tian, X., & Yoshida, T. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102611 · Cited by 103
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data
Lin, A., Jie Lu, B., Xuan, J., Zhu, F., & Zhang, G. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3379500 · Cited by 102
Heterogeneous Treatment Effects of Therapeutic-Dose Heparin in Patients Hospitalized for COVID-19
Goligher, E. C., Lawler, P. R., Jensen, T. P., Talisa, V. B., Berry, L. R., Lorenzi, E., McVerry, B. J., Chang, C. H., Leifer, E., Bradbury, C. A., et al. (2023) · JAMA · doi:10.1001/jama.2023.3651 · Cited by 103
Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference
Blakely, T., Lynch, J., Simons, K., Bentley, R., & Rose, S. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyz132 · Cited by 98
Machine Learning for Causal Inference: On the Use of Cross-fit Estimators
Zivich, P. N., & Breskin, A. (2021) · Epidemiology · doi:10.1097/ede.0000000000001332 · Cited by 100
The Dozen Things Experimental Economists Should Do (More of)
Czibor, E., Jimenez‐Gomez, D., & List, J. A. (2019) · Southern Economic Journal · doi:10.1002/soej.12392 · Cited by 102
Banerjee, M., Reynolds, E. L., Andersson, H. B., & Nallamothu, B. K. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.004879 · Cited by 107
Imbens, G. W. (2019) · National Bureau of Economic Research · doi:10.3386/w26104 · Cited by 103
Self-citationUplift Modeling for preventing student dropout in higher education
Olaya, D., Vásquez, J., Maldonado, S., Miranda, J., & Verbeke, W. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113320 · Cited by 97
Quasi-Oracle Estimation of Heterogeneous Treatment EffectsPreprint
Nie, X., & Wager, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.04912 · Cited by 97
Machine Learning Methods in Finance: Recent Applications and Prospects
Hoang, D., & Wiegratz, K. (2022) · European Financial Management · doi:10.1111/eufm.12408 · Cited by 127
Machine Learning for Prediction in Electronic Health Data
Rose, S. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.1404 · Cited by 98
High-dimensional regression adjustments in randomized experiments
Wager, S., Du, W., Taylor, J., & Tibshirani, R. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614732113 · Cited by 95
National Research Council (2011) · National Academies Press eBooks · doi:10.17226/13284 · Cited by 123
Local green finance policies and corporate ESG performance
Xue, Q., Wang, H., & Bai, C. (2023) · International Review of Finance · doi:10.1111/irfi.12417 · Cited by 94
Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation
Hitsch, G. J., Misra, S., & Zhang, W. W. (2018) · Quantitative Marketing and Economics · doi:10.1007/s11129-023-09278-5 · Cited by 106
Machine learning approaches to the social determinants of health in the health and retirement study
Seligman, B. J., Tuljapurkar, S. D., & Rehkopf, D. H. (2017) · SSM - Population Health · doi:10.1016/j.ssmph.2017.11.008 · Cited by 92
Causal Inference in the Social Sciences
Imbens, G. W. (2023) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-033121-114601 · Cited by 109
Self-citationSatellites reveal a small positive yield effect from conservation tillage across the US Corn Belt
Deines, J. M., Wang, S., & Lobell, D. B. (2019) · Environmental Research Letters · doi:10.1088/1748-9326/ab503b · Cited by 93
Metadata
- Authors
- Athey, S., & Imbens, G.
- Affiliations
- Stanford University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2016-07-05
- Volume
- 113
- Issue
- 27
- Pages
- 7353-7360
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC4941430
- OpenAlex
- W2305754340
- Cited by
- 1,586
- References
- 43
- Citations by year
- 2026: 213 · 2025: 235 · 2024: 198 · 2023: 175 · 2022: 170 · 2021: 178 · 2020: 161 · 2019: 110 · 2018: 66 · 2017: 61 · 2016: 15
- FWCI
- 87.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- Microsoft Research (0)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- treatment effect heterogeneity, recursive partitioning, regression trees, cross-validation, model selection criteria, high-dimensional covariates, causal inference
- MeSH terms
- Machine Learning, Computer Simulation, Models, Theoretical, Research Design, Statistics as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
