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A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a.
The central role of the propensity score in observational studies for causal effects
Rosenbaum, P. R. and Rubin, D. B · 1983
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Estimating causal effects from large data sets using propensity scores
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Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data
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The prognostic analogue of the propensity score
Hansen, B. B · 2008
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Causality
Pearl, J · 2009
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Regularization paths for generalized linear models via coordinate descent
Friedman, J., Hastie, T., and Tibshirani, R · 2010
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Collaborative double robust targeted maximum likelihood estimation
van der Laan, M. J. and Gruber, S · 2010
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Targeted learning: causal inference for observational and experimental data
Van der Laan, Mark J an d Rose, S · 2011
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Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies
Hainmueller, J · 2012
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Causal inference in statistics, social, and biomedical sciences
Imbens, G. W. and Rubin, D. B · 2015
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Learning representations for counterfactual inference
Johansson, F., Shalit, U., and Sontag, D · 2016
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Overlap in observational studies with high-dimensional covariates
D’Amour, A., Ding, P., Feller, A., Lei, L., and Sekhon, J · 2017
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On estimating regression-based causal effects using sufficient dimension reduction
Luo, W., Zhu, Y., and Ghosh, D · 2017
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R: A Language and Environment for Statistical Computing
R Core Team · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Entropy balancing is doubly robust
Zhao, Q. and Percival, D · 2017
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Approximate residual balancing: debiased inference of average treatment effects in high dimensions
Athey, S., Imbens, G. W., and Wager, S · 2018
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J · 2018
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Balanced policy evaluation and learning
Kallus, N · 2018
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Machine learning methods economists should know about
Athey, S. and Imbens, G · 2019
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Shalit, U., Johansson, F. D., and Sontag, D · 2017
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Outcome-adaptive lasso: Variable selection for causal inference
Shortreed, S. M. and Ertefaie, A · 2017
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Minimal dispersion approximately balancing weights: Asymptotic properties and practical considerations
Wang, Y. and Zubizarreta, J · 2017
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Rotnitzky, A. and Smucler, E · 2019
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Principles of confounder selection
VanderWeele, T. J · 2019
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Causal Inference
Hernán, M. A. and Robins, J. M · 2020
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