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Covariate adjustment is a ubiquitous method used to estimate the average treatment effect (ATE) from observational data.
1904
Earlier work this paper cites.
Kolmogoroff, A. (1933), Grundbegriffe der Wahrscheinlichkeitsrechnung
1933
Earlier work this paper cites.
Rosenbaum, P. R. & Rubin, D. B. (1983), ‘The central role of the propensity score in observational studies for causal effects’, Biometrika
1983
Earlier work this paper cites.
Powell, J. L., Stock, J. H. & Stoker, T. M. (1989), ‘Semiparametric estimation of index coefficients’, Econometrica: Journal of the Econometric Society
1989
Earlier work this paper cites.
Cox, C. S., Feldman, J. J., Golden, C. D., Lane, M. A., Madans, J. H., Mussolino, M. E. & Rothwell, S. T. (1997), ‘Plan and operation of the NHANES I epidemiologic follow-up study, 1992’, Vital and health statistics
1992
Earlier work this paper cites.
Ichimura, H. (1993), ‘Semiparametric least squares (SLS) and weighted SLS estimation of single-index models’, Journal of Econometrics
1993
Earlier work this paper cites.
Robins, J. M., Rotnitzky, A. & Zhao, L. P. (1994), ‘Estimation of regression coefficients when some regressors are not always observed’, Journal of the American statistical Association
1994
Earlier work this paper cites.
Robins, J. M. & Rotnitzky, A. (1995), ‘Semiparametric efficiency in multivariate regression models with missing data’, Journal of the American Statistical Association
1995
Earlier work this paper cites.
Van Rossum, G., Drake, F. L. et al. (1995), Python reference manual
1995
Earlier work this paper cites.
Hahn, J. (1998), ‘On the role of the propensity score in efficient semiparametric estimation of average treatment effects’, Econometrica
1998
Earlier work this paper cites.
Franklin, S. S., Khan, S. A., Wong, N. D., Larson, M. G. & Levy, D. (1999), ‘Is pulse pressure useful in predicting risk for coronary heart disease? The Framingham heart study’, Circulation
1999
Earlier work this paper cites.
van der Vaart, A. W. (2000), Asymptotic statistics
2000
Earlier work this paper cites.
Émery, M. & Schachermayer, W. (2001), On Vershik’s standardness criterion and Tsirelson’s notion of cosiness, in
2001
Earlier work this paper cites.
Delecroix, M., Härdle, W. & Hristache, M. (2003), ‘Efficient estimation in conditional single-index regression’, Journal of Multivariate Analysis
2003
Earlier work this paper cites.
Chickering, M., Heckerman, D. & Meek, C. (2004), ‘Large-sample learning of bayesian networks is NP-hard’, Journal of Machine Learning Research
2004
Earlier work this paper cites.
Hansen, B. B. (2008), ‘The prognostic analogue of the propensity score’, Biometrika
2008
Earlier work this paper cites.
Pearl, J. (2009), Causality
2009
Earlier work this paper cites.
van der Laan, M. J. & Gruber, S. (2010), ‘Collaborative double robust targeted maximum likelihood estimation’, The international journal of biostatistics
2010
Cited alongside, same era.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. & Duchesnay, E. (2011), ‘Scikit-learn: Machine learning in Python’, Journal of Machine Learning Research
2011
Cited alongside, same era.
van der Laan, M. J. & Rose, S. (2011), Targeted learning: causal inference for observational and experimental data
2011
Cited alongside, same era.
Uhler, C., Raskutti, G., Bühlmann, P. & Yu, B. (2013), ‘Geometry of the faithfulness assumption in causal inference’, The Annals of Statistics
2013
Cited alongside, same era.
Benkeser, D. & van der Laan, M. (2016), The highly adaptive lasso estimator, in
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. & Lee, S.-I. (2020), ‘From local explanations to global understanding with explainable AI for trees’, Nature machine intelligence
2020
Later among the works it cites.
Rotnitzky, A. & Smucler, E. (2020), ‘Efficient adjustment sets for population average causal treatment effect estimation in graphical models.’, Journal of Machine Learning Research
2020
Later among the works it cites.
Shah, R. D. & Peters, J. (2020), ‘The hardness of conditional independence testing and the generalised covariance measure’, The Annals of Statistics
2020
Later among the works it cites.
Veitch, V., Sridhar, D. & Blei, D. (2020), Adapting text embeddings for causal inference, in
2020
Later among the works it cites.
Bojer, C. S. & Meldgaard, J. P. (2021), ‘Kaggle forecasting competitions: An overlooked learning opportunity’, International Journal of Forecasting
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2016
Cited alongside, same era.
Baldé, I., Yang, Y. A. & Lefebvre, G. (2023), ‘Reader reaction to “Outcome-adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017)’, Biometrics
2017
Cited alongside, same era.
Billingsley, P. (2017), Probability and measure
2017
Cited alongside, same era.
Peters, J., Janzing, D. & Schölkopf, B. (2017), Elements of causal inference: foundations and learning algorithms
2017
Cited alongside, same era.
Shortreed, S. M. & Ertefaie, A. (2017), ‘Outcome-adaptive lasso: variable selection for causal inference’, Biometrics
2017
Cited alongside, same era.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. & Robins, J. (2018), ‘Double/debiased machine learning for treatment and structural parameters’, The Econometrics Journal
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Perković, E., Textor, J., Kalisch, M. & Maathuis, M. H. (2018), ‘Complete graphical characterization and construction of adjustment sets in markov equivalence classes of ancestral graphs’, Journal of Machine Learning Research
2018
Cited alongside, same era.
2021
Later among the works it cites.
Greenewald, K., Shanmugam, K. & Katz, D. (2021), High-dimensional feature selection for sample efficient treatment effect estimation, in
2021
Later among the works it cites.
Kallenberg, O. (2021), Foundations of Modern Probability
2021
Later among the works it cites.
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A. & Bengio, Y. (2021), ‘Toward causal representation learning’, Proceedings of the IEEE
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
Later among the works it cites.
Henckel, L., Perković, E. & Maathuis, M. H. (2022), ‘Graphical criteria for efficient total effect estimation via adjustment in causal linear models’, Journal of the Royal Statistical Society Series B: Statistical Methodology
2022
Later among the works it cites.
Christgau, A. M., Petersen, L. & Hansen, N. R. (2023), ‘Nonparametric conditional local independence testing’, The Annals of Statistics
2023
Later among the works it cites.
Forré, P. & Mooij, J. M. (2023), ‘A mathematical introduction to causality’
2023
Later among the works it cites.
2023
Later among the works it cites.
[Updated 2023 Jul 10]
Homan, T., Bordes, S. & Cichowski, E. (2024), ‘Physiology, pulse pressure’ · 2023
Later among the works it cites.
2023
Later among the works it cites.