Cited by 1,586Advanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods in Clinical Trials

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 access

Athey, Susan, Imbens, Guido·2016·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1510489113PMC PMC4941430

Open this paper in CoChat — free account Open PDF

Abstract

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%.

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

Crossref · 10.1073/pnas.1510489113

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Random Forests
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
Statistical Learning Theory
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

Show all 43 references

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).

Sorted by citations

Items 601–700 of 1,000

Parametric inference with universal function approximatorsPreprint
Joseph, A. C. (2019) · RePEc: Research Papers in Economics · Cited by 8
Recent Developments in Causal Inference and Machine LearningPreprint
Brand, J. E., Zhou, X., & Xie, Y. (2022) · doi:10.31235/osf.io/627tz · Cited by 8
Introducing DigiCAT: A digital tool to promote the principled use of counterfactual analysis for identifying potential active ingredients in mental health
Murray, A. L., Wright, H., Casey, H., Yang, Y., Zhu, X., Obsuth, I., Allitt, M., Mirman, D., Errington, P. J., & King, J. P. J. (2024) · Wellcome Open Research · doi:10.12688/wellcomeopenres.21105.1 · Cited by 7
Europeans’ attitudes toward the EU following Russia's invasion of Ukraine
Unan, A., & Klüver, H. (2024) · Political Science Research and Methods · doi:10.1017/psrm.2024.62 · Cited by 7
Personalized stress optimization intervention to reduce adolescents’ anxiety: A randomized controlled trial leveraging machine learning
Liu, J., Hu, J., Qi, Y., Wu, X., & Gan, Y. (2025) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2024.102964 · Cited by 8
Improving External Communication of Automated Vehicles Using Bayesian Optimization
Colley, M., Jansen, P., Keskar, M., & Rukzio, E. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714187 · Cited by 16
Safety evaluation of protected bike Lane treatments at Intersections: A causal framework
Dai, B., Wang, X., Guo, Q., Lu, Y., & Bai, Y. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108132 · Cited by 7
Transformer-variational autoencoder for estimating individual treatment effect using causal inference framework
Ahmad, S., & Wang, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06738-1 · Cited by 7
The effect of goal-setting prompts in a blended learning environment—evidence from a field experiment
Amann, E., & Rzepka, S. (2022) · Economics of Education Review · doi:10.1016/j.econedurev.2022.102331 · Cited by 7
Causal Inference with Latent Variables: Recent Advances and Future Prospectives
Zhu, Y.-C., He, Y.-H., Ma, J., Hu, M.-X., Li, S., & Li, J.-D. (2024) · Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671450 · Cited by 16
Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency
Bastos, L. D. S. L., Wortel, S. A., Bakhshi‐Raiez, F., Abu–Hanna, A., Dongelmans, D. A., Salluh, J. I., Zampieri, F. G., Burghi, G., Hamacher, S., Bozza, F. A., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105568 · Cited by 7
What Does Air Quality Information Disclosure Deliver and to Whom? Evidence from the Ambient Air Quality Standard (2012) Program in China
Deng, Y., Yue, Q., & Zhao, X. (2024) · Environmental and Resource Economics · doi:10.1007/s10640-024-00911-9 · Cited by 7
High-dimensional iterative causal forest (hdiCF) for subgroup identification using health care claims data
Wang, T., Pate, V., Wyss, R., Buse, J. B., Kosorok, M. R., & Stürmer‎, T. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae322 · Cited by 9
Predictors of rituximab efficacy in systemic sclerosis-associated interstitial lung disease: machine-learning analysis of the DESIRES trial
Kuzumi, A., Oba, K., Ebata, S., Kashiwabara, K., Ueda, K., Uemura, Y., Watadani, T., Fukasawa, T., Miura, S., Yoshizaki‐Ogawa, A., et al. (2024) · Lara D. Veeken · doi:10.1093/rheumatology/keae716 · Cited by 7
One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data
Lin, A., Lü, J., Xuan, J., Zhu, F., & Zhang, G. (2019) · Proceedings · doi:10.1109/icdm.2019.00052 · Cited by 7
Subgroup causal effect identification and estimation via matching tree
Zhang, Y., Schnell, P., Song, C. W., Huang, B., & Lü, B. (2021) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2021.107188 · Cited by 7
Effects of dialogue meetings on sickness absence—Evidence from a large field experiment
Alpino, M., Hauge, K. E., Kotsadam, A., & Markussen, S. (2022) · Journal of Health Economics · doi:10.1016/j.jhealeco.2022.102615 · Cited by 8
Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon health insurance experiment
Johnson, M. L., Cao, J., & Kang, H. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1535 · Cited by 7
Assessing the ability of an instrumental variable causal forest algorithm to personalize treatment evidence using observational data: the case of early surgery for shoulder fracture
Brooks, J. M., Chapman, C. G., Floyd, S. B., Chen, B., Thigpen, C. A., & Kissenberth, M. J. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01663-0 · Cited by 6
Toward Optimal Variance Reduction in Online Controlled Experiments
Jin, Y., & Ba, S. (2022) · Technometrics · doi:10.1080/00401706.2022.2142670 · Cited by 14
Mining Statistical Relations for Better Decision Making in Healthcare Processes
Koorn, J. J., Lu, X., Leopold, H., Martin, N., Verboven, S., & Reijers, H. A. (2022) · International Conference on Process Mining (ICPM) · doi:10.1109/icpm57379.2022.9980719 · Cited by 7
Business Analytics: Emerging Practice and Research Issues in the Health Insurance Industry
Terekhov, M. A., Demirezen, E. M., & Aytuğ, H. (2023) · Production and Operations Management · doi:10.1111/poms.13976 · Cited by 7
A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign
Sanisoğlu, M., Burnaz, Ş., & Kaya, T. (2023) · Journal of Marketing Analytics · doi:10.1057/s41270-023-00254-2 · Cited by 7
Calibration of Heterogeneous Treatment Effects in Randomized Experiments
Leng, Y., & Dimmery, D. (2024) · Information Systems Research · doi:10.1287/isre.2021.0343 · Cited by 7
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Sun, Z., He, B.-W., Ma, M., Tang, J., Wang, Y., Ma, C., & Liu, D.-G. (2023) · Industrial Conference on Data Mining · doi:10.1109/icdm58522.2023.00169 · Cited by 8
A data-adaptive method for investigating effect heterogeneity with high-dimensional covariates in Mendelian randomization
Tian, H., Tom, B. D. M., & Burgess, S. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02153-1 · Cited by 8
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
Simulation for Predicting Effectiveness and Safety of New Cardiovascular Drugs in Routine Care Populations
Najafzadeh, M., Schneeweiß, S., Choudhry, N. K., Wang, S. V., & Gagne, J. J. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1045 · Cited by 7
Estimating Heterogeneous Reactions to Experimental Treatments
Engel, C. (2019) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2020.07.011 · Cited by 7
Ten Rules for Conducting Retrospective Pharmacoepidemiological Analyses: Example COVID-19 Study
Powell, M. A., Koenecke, A., Byrd, J. B., Nishimura, A., Konig, M. F., Xiong, R., Mahmood, S., Mucaj, V., Bettegowda, C., Rose, L., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.700776 · Cited by 9
Self-citation
Leveraging machine learning methods to estimate heterogeneous effects: father absence in China as an example
Liu, R. (2021) · Chinese Sociological Review · doi:10.1080/21620555.2021.1948828 · Cited by 8
The Impact of Mobile App Failures on Online and Offline PurchasesPreprint
Narang, U., Shankar, V., & Narayanan, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3144434 · Cited by 7
Targeted UndersmoothingPreprint
Hansen, C., Kozbur, D., & Misra, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3167373 · Cited by 7
Uncovering Sociological Effect Heterogeneity Using Machine LearningPreprint
Brand, J. E., Xu, J., Koch, B., & Geraldo, P. (2019) · doi:10.31235/osf.io/x68hj · Cited by 7
Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment EffectsPreprint
Gao, Z., & Han, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06471 · Cited by 7
A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources.Preprint
Tan, X., Chang, C. H., & Tang, L. (2021) · arXiv (Cornell University) · Cited by 7
From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming LifecyclePreprint
Cong, Z., Liu, J., & Manchanda, P. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3878605 · Cited by 10
Mobile Internet Access and the Desire to EmigratePreprint
Aksoy, C. G., Adema, J. A. H., & Poutvaara, P. (2022) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/8rnp3 · Cited by 9
A fundamental measure of treatment effect heterogeneity
Levy, J., van der Laan, M. J., Hubbard, A., & Pirracchio, R. (2021) · Journal of Causal Inference · doi:10.1515/jci-2019-0003 · Cited by 23
Random Forest estimation of the ordered choice model
Lechner, M., & Okasa, G. (2024) · Empirical Economics · doi:10.1007/s00181-024-02646-4 · Cited by 6
Treatment Effects in Market Equilibrium
Munro, E., Xu, K., & Wager, S. (2025) · American Economic Review · doi:10.1257/aer.20230039 · Cited by 70
Variable importance measures for heterogeneous treatment effects
Hines, O., Diaz‐Ordaz, K., & Vansteelandt, S. (2025) · Biometrics · doi:10.1093/biomtc/ujaf140 · Cited by 6
What's Missing in Environmental Self-Monitoring: Evidence from Strategic Shutdowns of Pollution Monitors
Mu, Y., Rubin, E., & Zou, E. (2024) · The Review of Economics and Statistics · doi:10.1162/rest_a_01477 · Cited by 6
Heterogeneity in the effect of green financing constraints on labor investment efficiency: A causal forest approach
Liu, T., Liu, J., & Cheng, T. F. (2024) · Economic Modelling · doi:10.1016/j.econmod.2024.106977 · Cited by 6
Structural Substitution Rather Than Efficiency Improvement in Thermal Power Sector Achieved by China’s Pilot Carbon Markets
Qian, H., Gong, Y., Wang, S., & Wu, L. (2025) · The Energy Journal · doi:10.1177/01956574251315386 · Cited by 6
Data technologies and analytics for policy and governance: a landscape review
Asensio, O. I., Moore, C. E., Ulibarrí, N., Simsekler, M. C. E., Lan, T., & Rivero, G. (2025) · Data & Policy · doi:10.1017/dap.2024.49 · Cited by 9
Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support
Fuller, S., de la Cerda, N., & Rametta, J. T. (2025) · Political Behavior · doi:10.1007/s11109-025-10030-w · Cited by 6
Deep Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence
Ye, Z., Zhang, Z., Zhang, D., Zhang, H., & Zhang, R. (2025) · Management Science · doi:10.1287/mnsc.2024.04625 · Cited by 12
Research on the impact of ultra-high voltage transmission on urban carbon neutral technology innovation: An empirical test based on double machine learning method
Han, D., Wang, R., Yuan, Y., & Xiao, D. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.109120 · Cited by 6
Improved Inference for Doubly Robust Estimators of Heterogeneous Treatment Effects
Shin, H., & Antonelli, J. (2023) · Biometrics · doi:10.1111/biom.13837 · Cited by 6
Identifying heterogeneity using recursive partitioning: evidence from SMS nudges encouraging voluntary retirement savings in Mexico
Shah, A. M., Osborne, M., Lefkowitz, J., Fertig, A., Fishbane, A., & Soman, D. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad058 · Cited by 7
A Primer on Deep Learning for Causal Inference
Koch, B., Sainburg, T., Geraldo, P., Jiang, S., Sun, Y., & Foster, J. G. (2024) · Sociological Methods & Research · doi:10.1177/00491241241234866 · Cited by 17
The R.O.A.D. to precision medicine
Bertsimas, D., Koulouras, A. G., & Margonis, G. A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01291-6 · Cited by 6
Improving knowledge graphs via data-based causal structures
Lyu, D., Wang, X., Chen, L., Ban, T., Xiao, K., & Liu, L. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02414-5 · Cited by 6
Symmetry-Aware Transformers for Asymmetric Causal Discovery in Financial Time Series
Zheng, W., & Liu, W. (2025) · Symmetry · doi:10.3390/sym17101591 · Cited by 10
A comparison of resampling and recursive partitioning methods in random forest for estimating the asymptotic variance using the infinitesimal jackknife
Brokamp, C., Rao, M., Ryan, P., & Jandarov, R. (2017) · Stat · doi:10.1002/sta4.162 · Cited by 6
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation
Makar, M., Swaminathan, A., & Kıcıman, E. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014544 · Cited by 6
Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health
Huang, V., Morris, K., Jain, M., Ramesh, B. M., Kemp, H., Blanchard, J., Isac, S. K., Sarkar, B. K., Gothalwal, V., Namasivayam, V., et al. (2020) · BMJ Global Health · doi:10.1136/bmjgh-2020-002340 · Cited by 6
How R Helps Airbnb Make the Most of its Data
Bion, R. A. H., Chang, R., & Goodman, J. (2017) · The American Statistician · doi:10.1080/00031305.2017.1392362 · Cited by 6
Increasing the robustness of uplift modeling using additional splits and diversified leaf select
Oechsle, F. (2022) · Journal of Marketing Analytics · doi:10.1057/s41270-022-00186-3 · Cited by 6
Bayesian graphical modeling for heterogeneous causal effects
Castelletti, F., & Consonni, G. (2022) · Statistics in Medicine · doi:10.1002/sim.9599 · Cited by 6
Abnormal weather and loan bias: Evidence from China's FinTech credit market
Hao, X., Chen, F., & Chen, Z. (2023) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2023.107164 · Cited by 6
Treatment Effect Risk: Bounds and Inference
Kallus, N. (2023) · Management Science · doi:10.1287/mnsc.2023.4819 · Cited by 6
Understanding causal effects of ride-sourcing subsidy: A novel generative adversarial networks approach
Xie, N., Tang, W., Zhu, J., Li, J., & Chen, X. M. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104371 · Cited by 6
Multiple treatment effect estimation for business analytics using observational data
Tsuboi, Y., Sakai, Y., Shimizu, R., & Goto, M. (2024) · Cogent Engineering · doi:10.1080/23311916.2023.2300557 · Cited by 6
Deep Ensemble Transformers for Dimensionality Reduction
Nareklishvili, M., & Geitle, M. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3357621 · Cited by 6
MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects
Yang, X.-H., Liao, H.-J., Sun, P.-Y., Ma, J., Wang, B., He, Y., Xue, L., Su, L.-M., & Wang, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3379432 · Cited by 6
Comparison of Machine Learning Algorithms in Restaurant Revenue Prediction
Stepan, G., & Ozhegov, E. M. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39575-9_4 · Cited by 6
Evaluating heterogeneity of treatment effects
Vijan, S. S. (2020) · Biostatistics & Epidemiology · doi:10.1080/24709360.2020.1724003 · Cited by 6
The Zero Bias in Target Retirement Fund Choice
Kalra, A., Liu, X., & Zhang, W. (2020) · Journal of Consumer Research · doi:10.1093/jcr/ucaa035 · Cited by 6
Evaluating company bankruptcies using causal forests
Bittencourt, W. R., & Albuquerque, P. H. M. (2020) · Revista Contabilidade & Finanças · doi:10.1590/1808-057x202010360 · Cited by 7
Predictive Biomarker Identification for Biopharmaceutical Development
Huang, X., Li, H., Gu, Y., & Chan, I. S. F. (2020) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2020.1819404 · Cited by 6
Optimal data collection design in machine learning: the case of the fixed effects generalized least squares panel data model
Gnecco, G., Nutarelli, F., & Selvi, D. (2021) · Machine Learning · doi:10.1007/s10994-021-05976-x · Cited by 6
The Effect of COVID-19 Vaccinations on Self-Reported Depression and Anxiety During February 2021
Rubinstein, M., Haviland, A. M., & Breslau, J. A. (2023) · Statistics and Public Policy · doi:10.1080/2330443x.2023.2190008 · Cited by 6
Practical guidance on modeling choices for the virtual twins method
Deng, C., Wolf, J. M., Vock, D. M., Carroll, D. M., Boatman, J. A., Hatsukami, D. K., Leng, N., & Koopmeiners, J. S. (2023) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2023.2170404 · Cited by 6
Simple Tests for Selection: Learning More from Instrumental Variables
Black, D. A., Joo, J., LaLonde, R., Smith, J. A., & Taylor, E. J. (2022) · Labour Economics · doi:10.1016/j.labeco.2022.102237 · Cited by 6
Machine learning in agricultural economics
Baylis, K., Heckelei, T., & Storm, H. (2021) · Handbook of agricultural economics · doi:10.1016/bs.hesagr.2021.10.007 · Cited by 7
Living with offspring surely brings happiness to the elderly?—the heterogeneity in the effect of living arrangements on the life satisfaction of the Chinese elderly
Zhu, M. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.11.186 · Cited by 6
Condition Analysis and Forecasting in the Fashion Industry
Czekała, M. (2019) · International Journal of Economics Finance and Management Sciences · doi:10.11648/j.ijefm.20190702.14 · Cited by 6
Optimal Trade-Off Between Sample Size and Precision of Supervision for the Fixed Effects Panel Data Model
Gnecco, G., & Nutarelli, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-37599-7_44 · Cited by 6
A general framework for causal classification
Li, J., Zhang, W., Liu, L., Yu, K., Le, T. D., & Liu, J. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00249-1 · Cited by 5
Intervention Recommendation for Improving Disability Employment
Tran, H. X., Le, T. D., Li, J., Liu, L., Liu, J., Zhao, Y., & Waters, T. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378350 · Cited by 6
Measuring Changes in Disparity Gaps: An Application to Health Insurance
Goldsmith-Pinkham, P. S., Jiang, K. J.-R., Song, Z., & Wallace, J. (2022) · AEA Papers and Proceedings · doi:10.1257/pandp.20221111 · Cited by 6
Learning Treatment Effects in Panels with General Intervention PatternsPreprint
Farias, V. F., Li, A. A., & Peng, T. (2021) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2106.02780 · Cited by 7
Deep-Learning-Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical EvidencePreprint
Ye, Z., Zhang, Z., Zhang, D., Zhang, H., & Zhang, R. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4375327 · Cited by 6
Causal Inference and Policy Evaluation Without a Control GroupPreprint
Cerqua, A., Menchetti, F., & Letta, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5105143 · Cited by 6
Causal Effect Estimation on Imaging and Clinical Data for Treatment Decision Support of Aneurysmal Subarachnoid Hemorrhage
Ma, W., Chen, C., Gong, Y., Chan, N. Y., Jiang, M., Mak, C. H., Abrigo, J., & Dou, Q. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3390812 · Cited by 5
Mapping desert shrub aboveground biomass in the Junggar Basin, Xinjiang, China using quantile regression forest (QRF)
Yang, X. (2025) · PeerJ · doi:10.7717/peerj.19099 · Cited by 5
Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products
Athey, S. C. (2025) · American Economic Review · doi:10.1257/aer.115.4.1059 · Cited by 5
Self-citation
Precision ecology for targeted conservation action
Spake, R., Jackson, E. E., Bullock, J. M., Gardner, E., Tipton, E., Grainger, M., & Doncaster, C. P. (2025) · Nature Ecology & Evolution · doi:10.1038/s41559-025-02733-4 · Cited by 5
Heterogeneous and differential treatment effect analysis of safety improvements on freeways using causal inference
Zaidi, S. Z., Wang, X., Azati, Y., Li, J., Fan, T., & Quddus, M. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108173 · Cited by 5
Increasing Degree Attainment among Low-Income Students: The Role of Intensive Advising and College Quality
Barr, A., & Castleman, B. (2025) · American Economic Review · doi:10.1257/aer.20240669 · Cited by 5
Causal Inferences from Digital Behavioral Data
Leitgöb, H., & Keusch, F. (2026) · KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie · doi:10.1007/s11577-026-01050-3 · Cited by 6
Targeting resources efficiently and justifiably by combining causal machine learning and theory
Ali, Ö. G. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.1015604 · Cited by 7
Environmental Regulation, Smart Meter Adoption, and Carbon Emission: An Interpretable Machine Learning Approach
Gao, Y., Zhao, C., & Zhang, J. (2023) · Annual International Conference on Digital Government Research · doi:10.1145/3598469.3598531 · Cited by 5
Heterogeneous treatment effect of dose‐dense paclitaxel plus carboplatin therapy for advanced ovarian cancer
Taguchi, A., Kato, K., Furusawa, A., Hara, K., Sone, K., Yamada, K., Kajiyama, H., Shimada, M., & Okamoto, A. (2024) · International Journal of Cancer · doi:10.1002/ijc.34996 · Cited by 5
AI and green credit: A new catalyst for green innovation in Chinese enterprises
Hu, Z., Zhang, J. R., & Chang, C. (2024) · Oeconomia Copernicana · doi:10.24136/oc.3020 · Cited by 5
How do firms’ financial conditions influence the transmission of monetary policy? A non-parametric local projection approach
Paranhos, L. (2024) · Journal of Econometrics · doi:10.1016/j.jeconom.2024.105886 · Cited by 5
Fairness testing for uplift models
Lo, V. S. Y., Xu, Y., Li, Z., & Thielbar, M. (2024) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00339-6 · Cited by 5
A heterogeneity analysis of health-related quality of life in early adults born very preterm or very low birthweight across the sociodemographic spectrum
Bolbocean, C., Anderson, P. J., Bartmann, P., Cheong, J. L. Y., Doyle, L. W., Johnson, S. J., Marlow, N., Dieter Wolke, C., Petrou, S., & O’Neill, S. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.118181 · Cited by 5

Authors: Athey, S., & Imbens, G.
Affiliations: Stanford University (US)
ORCID: Athey, Susan
Imbens, Guido
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
DOI: 10.1073/pnas.1510489113
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
Licence: www.pnas.org/preview_site/misc/userlicense.xhtml (vor, 184 d)
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

Recursive partitioning for heterogeneous causal effects

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks