Metalearners for estimating heterogeneous treatment effects using machine learning

Keywords

Smoothness, Estimator, Computer science, Machine learning, Treatment effect

References

• Some methods for heterogeneous treatment effect estimation in high dimensionsCinthia Cassan — 2017
• Subgroup Identification and Variable Selection from Randomized Clinical Trial Data.Jared C. Foster — 2013
• A Distribution-Free Theory of Nonparametric RegressionLászló Györfi, Michael Köhler, Adam Krzyżak — 2002
• On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9Jerzy Splawa-Neyman, D. M. Dabrowska, Terence P. Speed — 1990
• Introduction to Nonparametric EstimationAlexandre B. Tsybakov — 2008
• Approximation dans les espaces métriques et théorie de l'estimationLucien Birg� — 1983
• Attributing Effects to a Cluster-Randomized Get-Out-the-Vote CampaignBen B. Hansen, Jake Bowers — 2009
• Estimation and Accuracy after Model SelectionBradley Efron — 2013
• The rank of the covariance matrix of an evanescent fieldMark Kliger, Joseph M. Francos — 2008
• Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme ImpactsJames J. Heckman, Jeffrey A. Smith, Nina Clements — 1997
• Is There Backlash to Social Pressure? A Large-scale Field Experiment on Voter MobilizationChristopher Mann — 2010
• Optimal Global Rates of Convergence for Nonparametric RegressionCharles J. Stone — 1982
• Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critiqueWinston T. Lin — 2012
• Covariate adjustment for two-sample treatment comparisons in randomized clinical trials: A principled yet flexible approachAnastasios A. Tsiatis, Marie Davidian, Min Zhang — 2008
• Bayesian Nonparametric Modeling for Causal InferenceJennifer Hill — 2011
• ROOT-N-CONSISTENT SEMIPARAMETRIC REGRESSIONPeter M. Robinson — 1988
• Generating random correlation matrices based on vines and extended onion methodDaniel Lewandowski, Dorota Kurowicka, Harry Joe — 2009
• Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression TreesDonald P. Green, Holger Kern — 2012
• BART: Bayesian Additive Regression TreesHugh A. Chipman, Edward I. George, Robert E. McCulloch — 2008
• Estimating causal effects of treatments in randomized and nonrandomized studies.Donald B. Rubin — 1974

Cited by

• Spillover Effects of Institutions on Cooperative Behavior, Preferences, and BeliefsFlorian Engl, Arno Riedl, Roberto A. Weber — 2017
• Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effectsP. Richard Hahn, Jared S. Murray, Carlos M. Carvalho — 2017
• Learning Objectives for Treatment Effect EstimationXinkun Nie, Stefan Wager — 2017
• A Minimax Surrogate Loss Approach to Conditional Difference EstimationSiong Thye Goh, Cynthia Rudin — 2018
• A Semiparametric Approach to Model Effect ModificationMuxuan Liang, Menggang Yu — 2018
• Representation Balancing MDPs for Off-Policy Policy EvaluationYao Liu, Omer Gottesman, Aniruddh Raghu — 2018
• Improving pairwise comparison models using Empirical Bayes shrinkageStephen Ragain, Alexander Peysakhovich, Johan Ugander — 2018
• Local Linear ForestsRina Friedberg, Julie Tibshirani, Susan Athey — 2018
• Transfer Learning for Estimating Causal Effects using Neural NetworksSören R. Künzel, Bradly C. Stadie, Nikita Vemuri — 2018
• Interval Estimation of Individual-Level Causal Effects Under Unobserved ConfoundingNathan Kallus, Xiaojie Mao, Angela Zhou — 2018
• Heterogeneous Treatment Effect Estimation through Deep LearningRan Chen, Hanzhong Liu — 2018
• Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidenceMichael C Knaus, Michael Lechner, Anthony Strittmatter — 2018
• Causaltoolbox—Estimator Stability for Heterogeneous Treatment EffectsSören R. Künzel, Simon J. S. Walter, Jasjeet S. Sekhon — 2018
• Quasi-oracle estimation of heterogeneous treatment effectsXinkun Nie, Stefan Wager — 2017
• Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset InterventionsFredrik Johansson — 2018
• Inferring Heterogeneous Causal Effects in Presence of Spatial ConfoundingMuhammad Osama, Dave Zachariah, Thomas B. Schön — 2019
• Classifying Treatment Responders Under Causal Effect MonotonicityNathan Kallus — 2019
• Orthogonal Statistical LearningDylan J. Foster, Vasilis Syrgkanis — 2019
• High-dimensional semi-supervised learning: in search for optimal inference of the meanYuqian Zhang, Jelena Bradić — 2019
• Estimating Treatment Effects with Causal Forests: An ApplicationSusan Athey, Stefan Wager — 2019

Related papers

• Named entity recognition in Chinese medical records based on cascaded conditional random fieldYan Yan — 2014
• Human behavior recognition based on fractal conditional random fieldZhuowen Lv, Kejun Wang — 2013
• Bidirectional integrated random fields for human behaviour understandingA.A. Liu — 2012
• WNUT 2020 Shared Task-1: Conditional Random Field(CRF) based Named Entity Recognition(NER) for Wet Lab ProtocolsKaushik Acharya — 2020
• Result identification for biomedical abstracts using Conditional Random FieldsRyan T.K. Lin, Hong-Jei Dai, Yue-Yang Bow — 2008
• Human behavior recognition based on stratified fractal conditional random fieldTao Yan — 2013
• Activity recognition using conditional random fieldMegha Agarwal, Peter Flach — 2015
• Rates of Expansions for Functional EstimatorsYulia Kotlyarova, Marcia M. A. Schafgans, Victoria Zinde‐Walsh — 2021
• RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fieldsGaoxiang Chen, Qun Li, Fuqian Shi — 2020