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There are many different causal effect estimators in causal inference.
A generalization of sampling without replacement from a finite universe
Horvitz, D. G. and Thompson, D. J · 1952
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Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B · 1974
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Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests
Friedman, J. H. and Rafsky, L. C · 1979
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Graph-theoretic measures of multivariate association and prediction
Friedman, J. H. and Rafsky, L. C · 1983
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An omnibus test for the two-sample problem using the empirical characteristic function
Epps, T. and Singleton, K. J · 1986
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Statistics and causal inference
Holland, P. W · 1986
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Evaluating the econometric evaluations of training programs with experimental data
LaLonde, R. J · 1986
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A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
Robins, J · 1986
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Causation, Prediction, and Search , volume 81
Spirtes, P., Glymour, C., and Scheines, R · 1993
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A probabilistic calculus of actions
Pearl, J · 1994
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Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs
Dehejia, R. H. and Wahba, S · 1999
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A Comparison of Experimental and Observational Data Analyses , chapter 5, pp. 49–60
Hill, J. L., Reiter, J. P., and Zanutto, E. L · 2004
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Can nonrandomized experiments yield accurate answers? a randomized experiment comparing random and nonrandom assignments
Shadish, W. R., Clark, M. H., and Steiner, P. M · 2008
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Causality
Pearl, J · 2009
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Bias-corrected matching estimators for average treatment effects
Abadie, A. and Imbens, G. W · 2011
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Bayesian nonparametric modeling for causal inference
Hill, J. L · 2011
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Scikit-learn: Machine learning in Python
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., and Duchesnay, E · 2011
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Implementation of G-Computation on a Simulated Data Set: Demonstration of a Causal Inference Technique
Snowden, J. M., Rose, S., and Mortimer, K. M · 2011
Cited alongside, same era.
The performance of estimators based on the propensity score
Huber, M., Lechner, M., and Wunsch, C · 2013
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Sensitivity of matching-based program evaluations to the availability of control variables
Lechner, M. and Wunsch, C · 2013
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Energy statistics: A class of statistics based on distances
Székely, G. J. and Rizzo, M. L · 2013
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Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases
Franklin, J., Schneeweiss, S., Polinski, J., and Rassen, J · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Removing hidden confounding by experimental grounding
Kallus, N., Puli, A. M., and Shalit, U · 2018
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Machine learning estimation of heterogeneous causal effects: Empirical monte carlo evidence, 2018
Knaus, M. C., Lechner, M., and Strittmatter, A · 2018
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Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis
Shimoni, Y., Yanover, C., Karavani, E., and Goldschmnidt, Y · 2018
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How well does your sampler really work?
Turner, R. and Neal, B · 2018
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Hyperparameter Importance Across Datasets , pp. 2367–2376
van Rijn, J. N. and Hutter, F · 2018
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Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases
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Cited alongside, same era.
Counterfactuals and Causal Inference: Methods and Principles for Social Research
Morgan, S. L. and Winship, C · 2014
Cited alongside, same era.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W. and Rubin, D. B · 2015
Cited alongside, same era.
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Ramdas, A., Reddi, S. J., Póczos, B., Singh, A., and Wasserman, L · 2015
Cited alongside, same era.
Causal inference in statistics: A primer
Pearl, J., Glymour, M., and Jewell, N. P · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
A pytorch library for differentiable two-sample tests
Djolonga, J · 2017
Cited alongside, same era.
Wendling, T., Jung, K., Callahan, A., Schuler, A., Shah, N. H., and Gallego, B · 2018
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Using wasserstein generative adversarial networks for the design of monte carlo simulations, 2019
Athey, S., Imbens, G., Metzger, J., and Munro, E · 2019
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Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Dorie, V., Hill, J., Shalit, U., Scott, M., and Cervone, D · 2019
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Atlantic causal inference conference (acic) data analysis challenge 2017, 2019
Hahn, P. R., Dorie, V., and Murray, J. S · 2019
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Metalearners for estimating heterogeneous treatment effects using machine learning
Künzel, S. R., Sekhon, J. S., Bickel, P. J., and Yu, B · 2019
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On the interpretation of do(x)
Pearl, J · 2019
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Adapting neural networks for the estimation of treatment effects
Shi, C., Blei, D., and Veitch, V · 2019
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An evaluation toolkit to guide model selection and cohort definition in causal inference, 2019
Shimoni, Y., Karavani, E., Ravid, S., Bak, P., Ng, T. H., Alford, S. H., Meade, D., and Goldschmidt, Y · 2019
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Causal Inference: What If
Hernán, M. A. and Robins, J. M · 2020
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Introduction to Causal Inference
Neal, B · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors · 2020
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