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While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction.
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Examining the impact of missing data on propensity score estimation in determining the effectiveness of self-monitoring of blood glucose (smbg)
D’Agostino, R., W. Lang, M. Walkup, T. Morgan, and A. Karter (2001, Dec) · 2001
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Efficient estimation of average treatment effects using the estimated propensity score
Hirano, K., G. W. Imbens, and G. Ridder (2003) · 2003
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Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: Finite sample oracle inequalities and examples
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Unified methods for censored longitudinal data and causality
van der Laan, M. J. and J. M. Robins (2003) · 2003
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Propensity score estimation with boosted regression for evaluating causal effects in observational studies
McCaffrey, D. F., Ridgeway, G. and Morral, A. R. (2004) · 2004
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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Lunceford, J. K. and M. Davidian (2004) · 2004
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Propensity score estimation with boosted regression for evaluating causal effects in observational studies
McCaffrey, D. F., G. Ridgeway, and A. R. Morral (2004) · 2004
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Doubly robust estimation in missing data and causal inference models
Bang, H. and J. M. Robins (2005) · 2005
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Causal inference using potential outcomes: Design, modeling, decisions
Rubin, D. B. (2005) · 2005
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Targeted maximum likelihood learning
van der Laan, M. J. and Rubin, D. (2006) · 2006
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Large sample properties of matching estimators for average treatment effects
Abadie, A. and G. W. Imbens (2006) · 2006
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Targeted maximum likelihood learning
Van Der Laan, M. J. and D. Rubin (2006) · 2006
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Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
Kang, J. D., J. L. Schafer, et al. (2007) · 2007
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Comment: Performance of double-robust estimators when" inverse probability" weights are highly variable
Robins, J., M. Sued, Q. Lei-Gomez, and A. Rotnitzky (2007) · 2007
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The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials
Rubin, D. B. (2007) · 2007
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Super learner
van der Laan, M. J., E. C. Polley, and A. E. Hubbard (2007, July) · 2007
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Can One Estimate The Unconditional Distribution of Post-Model-Selection Estimators?
Leeb, H. and M. P. Benedikt (2008) · 2008
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Evaluating uses of data mining techniques in propensity score estimation: a simulation study
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Some methods of propensity-score matching had superior performance to others: results of an empirical investigation and monte carlo simulations
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The consistency statement in causal inference: a definition or an assumption?
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Recent developments in the econometrics of program evaluation
Imbens, G. W. and J. M. Wooldridge (2009) · 2009
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Concerning the consistency assumption in causal inference
VanderWeele, T. J. (2009) · 2009
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Improving propensity score weighting using machine learning
Lee,B. K., Lessler, J. and Stuart,E. A. (2010) · 2010
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Super learner in prediction
Polley, E. C. and van der Laan, M. J. (2010) · 2010
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Collaborative double robust targeted maximum likelihood estimation
van der Laan, M. J. and Gruber, S. (2010) · 2010
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A targeted maximum likelihood estimator of a causal effect on a bounded continuous outcome
Gruber, S. and M. J. van der Laan (2010) · 2010
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Improving propensity score weighting using machine learning
Lee, B. K., J. Lessler, and E. A. Stuart (2010) · 2010
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On the consistency rule in causal inference: axiom, definition, assumption, or theorem?
Pearl, J. (2010) · 2010
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Super learner in prediction
Polley, E. C. and M. J. van der Laan (2010, May) · 2010
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Matching methods for causal inference: A review and a look forward
Stuart, E. A. (2010) · 2010
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Collaborative double robust targeted maximum likelihood estimation
Van Der Laan, M. J. and S. Gruber (2010) · 2010
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Propensity score estimation: neural networks, support vector machines, decision trees (cart), and meta-classifiers as alternatives to logistic regression
Westreich, D., J. Lessler, and M. J. Funk (2010) · 2010
The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bcart, and the covariate-balancing propensity score
Wyss, R., A. R. Ellis, M. A. Brookhart, C. J. Girman, M. Jonsson Funk, R. LoCasale, and T. Stürmer (2014) · 2014
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Prediction policy problems
Kleinberg, J., Ludwig, J., Mullainathan, S., and Obermeyer, Z. (2015) · 2015
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Machine learning methods for demand estimation
Bajari, P., D. Nekipelov, S. P. Ryan, and M. Yang (2015) · 2015
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Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression
Bartlett, J. W., O. Harel, and J. R. Carpenter (2015) · 2015
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Robust inference on average treatment effects with possibly more covariates than observations
Farrell, M. H. (2015) · 2015
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Targeted learning: causal inference for observational and experimental data
van der Laan, M. J., and Rose, S. (2011) · 2011
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Bias-corrected matching estimators for average treatment effects
Abadie, A. and G. W. Imbens (2011) · 2011
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L1-penalized quantile regression in high-dimensional sparse models
Belloni, A., V. Chernozhukov, et al. (2011) · 2011
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tmle: An r package for targeted maximum likelihood estimation
Gruber, S. and M. J. van der Laan (2011) · 2011
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Bayesian nonparametric modeling for causal inference
Hill, J. L. (2011) · 2011
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Genetic optimization using derivatives: the rgenoud package for r
Mebane Jr, W. R., J. S. Sekhon, et al. (2011) · 2011
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Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets
Gruber, S., R. W. Logan, I. Jarrín, S. Monge, and M. A. Hernán (2015) · 2015
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Prediction policy problems
Kleinberg, J., J. Ludwig, S. Mullainathan, and Z. Obermeyer (2015) · 2015
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Improving propensity score estimators’ robustness to model misspecification using super learner
Pirracchio, R., M. L. Petersen, and M. van der Laan (2015) · 2015
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Can big data solve the fundamental problem of causal inference?
Titiunik, R. (2015) · 2015
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Targeted learning of the mean outcome under an optimal dynamic treatment rule
van der Laan, M. J. and A. R. Luedtke (2015) · 2015
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A boosting algorithm for estimating generalized propensity scores with continuous treatments
Zhu, Y., D. L. Coffman, and D. Ghosh (2015) · 2015
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Matching on the estimated propensity score
Abadie, A. and G. W. Imbens (2016) · 2016
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Recursive partitioning for heterogeneous causal effects
Athey, S. and G. Imbens (2016) · 2016
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Semiparametric Theory and Empirical Processes in Causal Inference
Kennedy, E. H. (2016) · 2016
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Evaluating treatment effectiveness under model misspecification: a comparison of targeted maximum likelihood estimation with bias-corrected matching
Kreif, N., S. Gruber, R. Radice, R. Grieve, and J. S. Sekhon (2016) · 2016
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{TMLE} for Marginal Structural Models Based on an Instrument
Tóth, B. and M. J. van der Laan (2016) · 2016
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The state of applied econometrics: Causality and policy evaluation
Athey, S. and Imbens, G. W. (2017a) · 2017
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Machine learning: an applied econometric approach
Mullainathan, S. and Spiess, J. (2017) · 2017
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The state of applied econometrics: Causality and policy evaluation
Athey, S. and G. W. Imbens (2017) · 2017
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Athey, S. and S. Wager (2017) · 2017
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Double/debiased/neyman machine learning of treatment effects
Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, and W. Newey (2017) · 2017
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Chernozhukov, V., M. Goldman, V. Semenova, and M. Taddy (2017) · 2017
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Dorie, V., J. Hill, U. Shalit, M. Scott, and D. Cervone (2017) · 2017
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Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
Hahn, P. R., J. S. Murray, and C. M. Carvalho (2017) · 2017
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Scalable collaborative targeted learning for high-dimensional data
Ju, C., S. Gruber, S. D. Lendle, A. Chambaz, J. M. Franklin, R. Wyss, S. Schneeweiss, and M. J. van der Laan (2017) · 2017
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Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data
Ju, C., R. Wyss, J. M. Franklin, S. Schneeweiss, J. Häggström, and M. J. van der Laan (2017) · 2017
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Balanced policy evaluation and learning
Kallus, N. (2017) · 2017
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Who should be treated? empirical welfare maximization methods for treatment choice
Kitagawa, T. and A. Tetenov (2017) · 2017
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Faster rates for policy learning
Luedtke, A. and A. Chambaz (2017) · 2017
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Machine learning: an applied econometric approach
Mullainathan, S. and J. Spiess (2017) · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Wager, S. and S. Athey (2017) · 2017
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
Athey, S. and Imbens, G. W., and Wager, S. (2018) · 2018
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Newey, W. and Robins, J. (2018) · 2018
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
Athey, S., G. W. Imbens, and S. Wager (2018) · 2018
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W. Newey, and J. Robins (2018) · 2018
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Data-adaptive doubly robust instrumental variable methods for treatment effect heterogeneity
DiazOrdaz, K., R. Daniel, and N. Kreif (2018, February) · 2018
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High-dimensional doubly robust tests for regression parameters
Dukes, O., V. Avagyan, and S. Vansteelandt (2018) · 2018
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Variable selection – A review and recommendations for the practicing statistician
Heinze, G., C. Wallisch, and D. Dunkler (2018) · 2018
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Evaluating the impact of public health insurance reforms on infant mortality in Indonesia
Kreif, N., A. Mirelman, R. Moreno Serra, B. Hidayat, D. Erlannga, and M. Suhrcke (2018) · 2018
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The Balance Super Learner: A robust adaptation of the Super Learner to improve estimation of the average treatment effect in the treated based on propensity score matching
Pirracchio, R. and M. Carone (2018) · 2018
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Introduction to Double Robust Methods for Incomplete Data
Seaman, S. R. and S. Vansteelandt (2018) · 2018
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