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Domain adaptation and covariate shift are big issues in deep learning and they ultimately affect any causal inference algorithms that rely on deep neural networks.
A method for assessing the quality of a randomized control trial
T. C. Chalmers, Jr Smith, H., B. Blackburn, B. Silverman, B. Schroeder, D. Reitman, and A. Ambroz · 1981
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Evaluating the econometric evaluations of training programs with experimental data
Robert J. LaLonde · 1986
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Bayesian analysis in expert systems
Judea Pearl · 1993
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Estimation of regression coefficients when some regressors are not always observed
James M. Robins, Andrea Rotnitzky, and Lue Ping Zhao · 1994
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Identification of causal effects using instrumental variables
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Improving predictive inference under covariate shift by weighting the log-likelihood function
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Designing a research project: randomised controlled trials and their principles
J M Kendall · 2003
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Instruments for causal inference: An epidemiologist’s dream?
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Estimating causal effects from epidemiological data
Miguel A Hernán and James M Robins · 2006
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New algorithms for efficient high-dimensional nonparametric classification
Ting Liu, Andrew W. Moore, and Alexander Gray · 2006
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A critical appraisal of propensity score matching in the medical literature between 1996 and 2003
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Randomized clinical trials and observational studies: guidelines for assessing respective strengths and limitations
Edward L Hannan · 2008
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Using the whole cohort in the analysis of case-cohort data
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Causality
Judea Pearl · 2009
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Causality: Models, Reasoning and Inference
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Bayesian nonparametric modeling for causal inference
Jennifer Lynn Hill · 2010
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An introduction to causal inference
Judea Pearl · 2010
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Poor economics: A radical rethinking of the way to fight global poverty
Banerjee Abhijit V. and Duflo Esther · 2012
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On measurement bias in causal inference
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On identifying total effects in the presence of latent variables and selection bias
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Stochastic backpropagation and approximate inference in deep generative models
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Instrumental variables: An econometrician’s perspective
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Anchor regression: Heterogeneous data meet causality
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Representation learning for treatment effect estimation from observational data
Zhang Yao, Bellot Alexis, and Schaar Mihaela van der · 2018
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Inverse propensity score weighting with a latent class exposure: Estimating the causal effect of reported reasons for alcohol use on problem alcohol use 16 years later
Bethany C. Bray, John J. Dziak, Megan E. Patrick, and Stephanie T. Lanza · 2019
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Propensity score weighting for causal inference with multiple treatments
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Do ImageNet classifiers generalize to ImageNet?
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Measurement bias and effect restoration in causal inference
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Causal inference in the age of decision medicine
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Causal inference for clinicians
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A survey on recent advances in named entity recognition from deep learning models
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The blessings of multiple causes
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Robust causal inference under covariate shift via worst-case subpopulation treatment effects
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Learning overlapping representations for the estimation of individualized treatment effects
Zhang Yao, Bellot Alexis, and Schaar Mihaela van der · 2020
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Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms
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Causal inference methods for combining randomized trials and observational studies: a review
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Pitfalls of static language modelling
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Nonparametric identification is not enough, but randomized controlled trials are
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