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@inproceedings{macforman98,
author = {MacDorman, M. F. and Atkinso, J. O.},
booktitle = {Mon Vital Stat Rep, 46 (suppl 2)},
pages = {1--22},
title = {{Infant mortality statistics from the linked birth/infant death}},
year = {1998}
}
@article{Hill2013,
abstract = {Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there may exist neighborhoods of the covariate space where there are not sufficient numbers of both groups of women (those who breastfed for prolonged periods and those who did not) to make inferences about those women. This is referred to as lack of common support. Problems can arise when we try to estimate causal effects for units that lack common support, thus we may want to avoid inference for such units. If ignorability is satisfied with respect to a set of potential confounders, then identifying whether, or for which units, the common support assumption holds is an empirical question. However, in the high-dimensional covariate space often required to satisfy ignorability such identification may not be trivial. Existing methods used to address this problem often require reliance on parametric assumptions and most, if not all, ignore the information embedded in the response variable.We distinguish between the concepts of "common support" and "common causal support." We propose a new approach for identifying common causal support that addresses some of the shortcomings of existing methods. We motivate and illustrate the approach using data from the National Longitudinal Survey of Youth to estimate the effect of breastfeeding at least nine months on reading and math achievement scores at age five or six. We also evaluate the comparative performance of this method in hypothetical examples and simulations where the true treatment effect is known. {\textcopyright} Institute of Mathematical Statistics, 2013.},
author = {Hill, J. L. and Su, Y. S.},
file = {:U$\backslash$:/PhD/Papers/Causality/ASSESSING  LACK  OF  COMMON  SUPPORT  IN  CAUSALINFERENCE  USING  BAYESIAN  NONPARAMETRICS.pdf:pdf},
journal = {Annals of Applied Statistics},
keywords = {BART,Breastfeeding,Common support,Overlap,Propensity scores},
number = {3},
pages = {1386--1420},
title = {{Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes}},
volume = {7},
year = {2013}
}
@article{LaLonde1986,
abstract = {This paper compares the effect on trainee earnings of an employment program that was run as a field experiment where participants were randomly assigned to treatment and control groups with the estimates that would have been produced by an econometrician. This comparison shows that many of the econometric proce- dures do not replicate the experimentally determined results, and it suggests that researchers should be aware of the potential for specification errors in other nonexperimental evaluations.},
author = {LaLonde, R. J.},
file = {:U$\backslash$:/PhD/Papers/Causality/Datasets/Evaluating the Econometric Evaluations of Training Programs with Experimental Data.pdf:pdf},
journal = {The American Economic Review},
number = {4},
pages = {604--620},
title = {Evaluating the econometric evaluations of training programs with experimental data},
volume = {76},
year = {1986}
}
@book{Vandervaart98,
author = {van der Vaart, A. W.},
doi = {10.1017/CBO9780511802256},
isbn = {9780521496032},
month = {},
publisher = {Cambridge University Press},
title = {{Asymptotic statistics}},
url = {https://www.cambridge.org/core/product/identifier/9780511802256/type/book},
year = {1998}
}
@article{Breiman2001,
title={Random forests},
author={Breiman, L.},
journal={Machine learning},
volume={45},
number={1},
pages={5--32},
year={2001},
publisher={Springer}
}
@article{Farrell2018b,
abstract = {We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second-step inference after first-step estimation with deep learning, a result also new to the literature. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit activation function and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, such as treatment effects, expected welfare, and decomposition effects. Inference in many other semiparametric contexts can be readily obtained. We demonstrate the effectiveness of deep learning with a Monte Carlo analysis and an empirical application to direct mail marketing.},
archivePrefix = {arXiv},
arxivId = {1809.09953},
author = {Farrell, Max H. and Liang, Tengyuan and Misra, Sanjog},
eprint = {1809.09953},
file = {:U$\backslash$:/PhD/Papers/Causality/deep{\_}neural{\_}networks{\_}for{\_}estimation{\_}and{\_}inference{\_}application{\_}to{\_}causal{\_}effects{\_}and{\_}other{\_}semiparametric{\_}estimands.pdf:pdf},
keywords = {deep learning,inference,nonasymptotic bounds,program evaluation,rectified linear unit,semiparametric,treatment effects,treatment targeting},
title = {{Deep Neural Networks for Estimation and Inference}},
url = {http://arxiv.org/abs/1809.09953},
year = {2018}
}
@book{VanderLaan2018a,
author = {van der Laan, M. J. and Rose, S.},
doi = {10.1007/978-3-319-65304-4},
file = {:U$\backslash$:/PhD/Papers/Causality/2011{\_}Book{\_}TargetedLearning.pdf:pdf},
isbn = {978-3-319-65303-7},
title = {{Targeted learning in data science}},
url = {https://www.springer.com/gp/book/9783319653037{\%}0Ahttp://link.springer.com/10.1007/978-3-319-65304-4},
year = {2018},
publisher={Springer}
}
@article{Funk2011,
abstract = {Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/∼mfunk/dr/. {\textcopyright} 2011 The Author.},
author = {Funk, M. J. and Westreich, D. and Wiesen, C. and St{\"{u}}rmer, T. and Brookhart, M. A. and Davidian, M.},
doi = {10.1093/aje/kwq439},
file = {:U$\backslash$:/PhD/Papers/Causality/Doubly Robust Estimation of Causal Effects.pdf:pdf},
issn = {00029262},
journal = {American Journal of Epidemiology},
keywords = {causal inference,epidemiologic methods,propensity score},
number = {7},
pages = {761--767},
title = {{Doubly robust estimation of causal effects}},
volume = {173},
year = {2011}
}
@inproceedings{Futoma2017,
abstract = {We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiological time series using multitask Gaussian processes, seamlessly accounting for the high uncertainty, frequent missingness, and irregular sampling rates typically associated with real clinical data. The Gaussian process is directly connected to a black-box classifier that predicts whether a patient will become septic, chosen in our case to be a recurrent neural network to account for the extreme variability in the length of patient encounters. We show how to scale the computations associated with the Gaussian process in a manner so that the entire system can be discriminatively trained end-to-end using backpropagation. In a large cohort of heterogeneous inpatient encounters at our university health system we find that it outperforms several baselines at predicting sepsis, and yields 19.4{\%} and 55.5{\%} improved areas under the Receiver Operating Characteristic and Precision Recall curves as compared to the NEWS score currently used by our hospital.},
archivePrefix = {arXiv},
arxivId = {1706.04152},
author = {Futoma, J. and Hariharan, S. and Heller, K.},
eprint = {1706.04152},
file = {:U$\backslash$:/PhD/Papers/Causality/Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier.pdf:pdf},
isbn = {9781510855144},
booktitle = {34th International Conference on Machine Learning},
pages = {1914--1922},
title = {{Learning to detect sepsis with a multitask Gaussian process RNN classifier}},
volume = {},
year = {2017}
}
@article{Farrell2018a,
abstract = {We study deep neural networks and their use in semiparametric inference. We prove valid inference after first-step estimation with deep learning, a result new to the literature. We provide new rates of convergence for deep feedforward neural nets and, because our rates are sufficiently fast (in some cases minimax optimal), obtain valid semiparametric inference. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit activation function and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish nonasymptotic bounds for these deep nets for nonparametric regression, covering the standard least squares and logistic losses in particular. We then apply our theory to develop semipara-metric inference, focusing on treatment effects, expected welfare, and decomposition effects for concreteness. Inference in many other semiparametric contexts can be readily obtained. We demonstrate the effectiveness of deep learning with a Monte Carlo analysis and an empirical application to direct mail marketing.},
archivePrefix = {arXiv},
arxivId = {1809.09953v2},
author = {Farrell, Max H and Liang, Tengyuan and Misra, Sanjog},
eprint = {1809.09953v2},
file = {:U$\backslash$:/PhD/Papers/Causality/Deep Neural Networks for Estimation and Inferenc.pdf:pdf},
journal = {Working Paper},
keywords = {deep learning,inference,nonasymptotic bounds,program evaluation,rectified linear unit,semiparametric,treatment effects,treatment targeting},
title = {{Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands}},
year = {2018}
}
@article{Vansteelandt2014a,
abstract = {Structural nested models (SNMs) and the associated method of G-estimation were first proposed by James Robins over two decades ago as approaches to modeling and estimating the joint effects of a sequence of treatments or exposures. The models and estimation methods have since been extended to dealing with a broader series of problems, and have considerable advantages over the other methods developed for estimating such joint effects. Despite these advantages, the application of these methods in applied research has been relatively infrequent; we view this as unfortunate. To remedy this, we provide an overview of the models and estimation methods as developed, primarily by Robins, over the years. We provide insight into their advantages over other methods, and consider some possible reasons for failure of the methods to be more broadly adopted, as well as possible remedies. Finally, we consider several extensions of the standard models and estimation methods.},
archivePrefix = {arXiv},
arxivId = {arXiv:1503.01589v1},
author = {Vansteelandt, S. and Joffe, M.},
doi = {10.1214/14-STS493},
eprint = {arXiv:1503.01589v1},
file = {:U$\backslash$:/PhD/Papers/Causality/Over Time/Structural Nested Models andG-estimation The Partially RealizedPromise.pdf:pdf},
issn = {08834237},
journal = {Statistical Science},
keywords = {Causal effect,Confounding,Direct effect,Instrumental variable,Mediation,Time-varying confounding},
number = {4},
pages = {707--731},
title = {{Structural nested models and G-estimation: The partially realized promise}},
volume = {29},
year = {2014}
}
@inproceedings{Louizos2017a,
  title={Causal effect inference with deep latent-variable models},
  author={Louizos, C. and Shalit, U. and Mooij, J. M. and Sontag, D. and Zemel, R. and Welling, M.},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6446--6456},
  year={2017}
}
@article{Robins2000a,
abstract = {In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.},
author = {Robins, J. M. and Hern{\'{a}}n, M. A. and Brumback, B.},
doi = {10.1097/00001648-200009000-00011},
file = {:U$\backslash$:/PhD/Papers/Causality/Marginal{\_}Structural{\_}Models{\_}and{\_}Causal{\_}Inference{\_}in.11.pdf:pdf},
issn = {10443983},
journal = {Epidemiology},
keywords = {Causality,Confounding,Counterfactuals,Epidemiologic methods,Intermediate variables,Longitudinal data,Structural models},
number = {5},
pages = {550--560},
pmid = {10955408},
title = {{Marginal structural models and causal inference in epidemiology}},
volume = {11},
year = {2000}
}
@article{Kang2007,
abstract = {When outcomes are missing for reasons beyond an investigator's control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to missingness. One approach is to model the relationships between the covariates and the outcome and use those relationships to predict the missing values. Another is to model the probabilities of missingness given the covariates and incorporate them into a weighted or stratified estimate. Doubly robust (DR) procedures apply both types of model simultaneously and produce a consistent estimate of the parameter if either of the two models has been correctly specified. In this article, we show that DR estimates can be constructed in many ways. We compare the performance of various DR and non-DR estimates of a population mean in a simulated example where both models are incorrect but neither is grossly misspecified. Methods that use inverse-probabilities as weights, whether they are DR or not, are sensitive to misspecification of the propensity model when some estimated propensities are small. Many DR methods perform better than simple inverse-probability weighting. None of the DR methods we tried, however, improved upon the performance of simple regression-based prediction of the missing values. This study does not represent every missing-data problem that will arise in practice. But it does demonstrate that, in at least some settings, two wrong models are not better than one. {\textcopyright} Institute of Mathematical Statistics, 2007.},
archivePrefix = {arXiv},
arxivId = {arXiv:0804.2958v1},
author = {Kang, J. D. Y. and Schafer, J. L.},
doi = {10.1214/07-STS227},
eprint = {arXiv:0804.2958v1},
file = {:U$\backslash$:/PhD/Papers/Causality/Demystifying Double Robustness A Comparison of Alternative Strategiesfor Estimating a Population Mean fromIncomplete Data.pdf:pdf},
issn = {08834237},
journal = {Statistical Science},
keywords = {Causal inference,Missing data,Model-assisted survey estimation,Propensity score,Weighted estimating equations},
number = {4},
pages = {523--539},
title = {{Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data}},
volume = {22},
year = {2007}
}
@incollection{Kennedy2016,
abstract = {In this paper we review important aspects of semiparametric theory and empirical processes that arise in causal inference problems. We begin with a brief introduction to the general problem of causal inference, and go on to discuss estimation and inference for causal effects under semiparametric models, which allow parts of the data-generating process to be unrestricted if they are not of particular interest (i.e., nuisance functions). These models are very useful in causal problems because the outcome process is often complex and difficult to model, and there may only be information available about the treatment process (at best). Semiparametric theory gives a framework for benchmarking efficiency and constructing estimators in such settings. In the second part of the paper we discuss empirical process theory, which provides powerful tools for understanding the asymptotic behavior of semiparametric estimators that depend on flexible nonparametric estimators of nuisance functions. These tools are crucial for incorporating machine learning and other modern methods into causal inference analyses. We conclude by examining related extensions and future directions for work in semiparametric causal inference.},
archivePrefix = {arXiv},
arxivId = {1510.04740},
author = {Kennedy, E. H.},
doi = {10.1007/978-3-319-41259-7_8},
eprint = {1510.04740},
file = {:U$\backslash$:/PhD/Papers/Causality/GEstimation/semiparametricTheoryinCausalInference.pdf:pdf},
keywords = {chine learning,donsker class,efficient influence function,estimating equation,ma-,nonparametric theory},
pages = {141--167},
title = {{Semiparametric theory and empirical processes in causal inference}},
year = {2016},
booktitle={Statistical causal inferences and their applications in public health research}
}
@book{imbens2015causal,
author = {Imbens, G. W. and Rubin, D. B.},
publisher = {Cambridge University Press},
title = {{Causal inference in statistics, social, and biomedical sciences}},
year = {2015}
}
@book{ledoux2013probability,
  title={Probability in Banach Spaces: isoperimetry and processes},
  author={Ledoux, M. and Talagrand, M.},
  year={2013},
  publisher={Springer}
}
@article{Crump2008a,
abstract = {In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, that is, that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs. {\textcopyright} 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.},
author = {Crump, R. K. and Hotz, V. J. and Imbens, G. W. and Mitnik, O. A.},
doi = {10.1162/rest.90.3.389},
file = {:U$\backslash$:/PhD/Papers/Causality/Nonparametric tests fortreatment effect heterogeneity.pdf:pdf},
issn = {00346535},
journal = {Review of Economics and Statistics},
number = {3},
pages = {389--405},
title = {{Nonparametric tests for treatment effect heterogeneity}},
volume = {90},
year = {2008}
}
@inproceedings{Urteaga2017,
abstract = {In this paper, we introduce a novel task for machine learning in healthcare, namely personalized modeling of the female hormonal cycle. The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system. Because there are individual differences in the menstrual cycle, we are particularly interested in personalized models that can account for individual idiosyncracies, towards identifying phenotypes of menstrual cycles. As a first step, we consider the hormonal cycle as a set of observations through time. We use a previously validated mechanistic model to generate realistic hormonal patterns, and experiment with Gaussian process regression to estimate their values over time. Specifically, we are interested in the feasibility of predicting menstrual cycle phases under varying learning conditions: number of cycles used for training, hormonal measurement noise and sampling rates, and informed vs. agnostic sampling of hormonal measurements. Our results indicate that Gaussian processes can help model the female menstrual cycle. We discuss the implications of our experiments in the context of modeling the female menstrual cycle.},
archivePrefix = {arXiv},
arxivId = {1712.00117},
author = {Urteaga, I. and Albers, D. J. and Wheeler, M. V. and Druet, A. and Raffauf, H. and Elhadad, N.},
eprint = {1712.00117},
file = {:U$\backslash$:/PhD/Papers/Causality/Towards Personalized Modeling of the FemaleHormonal Cycle.pdf:pdf},
number = {},
title = {{Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes}},
url = {http://arxiv.org/abs/1712.00117},
year = {2017},
booktile = {Advances in Neural Information Processing Systems}
}
@article{Starr2011,
abstract = {file:///C:/Users/Marika/Desktop/universit{\`{a}} di pavia/articoli/strategie per l'infertilit{\`{a}}/nihms964087.pdf},
author = {Starr, Daniel A.},
doi = {10.1016/j.physbeh.2017.03.040},
file = {:U$\backslash$:/PhD/Papers/Causality/Assessing methods for generalizing experimental impact estimates to target populations.pdf:pdf},
isbn = {2163684814},
journal = {Physiology {\&} behavior},
keywords = {determination,protein crystallography,protein data bank,r -factor,resolution,restraints,structure,structure interpretation,structure quality,structure refinement,structure validation},
number = {1},
pages = {139--148},
title = {{乳鼠心肌提取 HHS Public Access}},
volume = {176},
year = {2011}
}
@inproceedings{Johansson2016,
abstract = {Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.},
archivePrefix = {arXiv},
arxivId = {1605.03661},
author = {Johansson, F. D. and Shalit, U. and Sontag, D.},
eprint = {1605.03661},
file = {:U$\backslash$:/PhD/Papers/Causality/Learning Representations for Counterfactual Inference.pdf:pdf},
isbn = {9781510829008},
booktitle = {33rd International Conference on Machine Learning, ICML 2016},
pages = {4407--4418},
title = {{Learning representations for counterfactual inference}},
volume = {},
year = {2016}
}
@inproceedings{Kandasamy2015,
abstract = {We propose and analyse estimators for statistical functionals of one or more distributions under nonparametric assumptions. Our estimators are derived from the von Mises expansion and are based on the theory of influence functions, which appear in the semiparametric statistics literature. We show that estimators based either on data-splitting or a leave-one-out technique enjoy fast rates of convergence and other favorable theoretical properties. We apply this framework to derive estimators for several popular information theoretic quantities, and via empirical evaluation, show the advantage of this approach over existing estimators.},
author = {Kandasamy, K. and Krishnamurthy, A. and P{\'{o}}czos, B. and Wasserman, L. and Robins, J. M.},
file = {:U$\backslash$:/PhD/Papers/Semiparametric Estimation Theory/Nonparametric von Mises Estimators for Entropies,Divergences and Mutual Informations.pdf:pdf},
issn = {10495258},
booktitle = {Advances in Neural Information Processing Systems},
pages = {397--405},
title = {{Nonparametric von Mises estimators for entropies, divergences and mutual informations}},
volume = {},
year = {2015}
}
@article{Pavlov2019a,
abstract = {The role of probabilistic methods in discrete mathematics cannot be overestimated. By defining the probability measure on a set of the studied combinatorial objects various numerical characteristics of these objects can be considered as random variables and studied using the methods of probability theory. The advantage of this approach is the well-developed probabilistic analytic techniques that allow us in many cases to obtain results, the proof of which by other methods appears too complicated, if indeed it is at all possible. The application of probabilistic methods is connected with extensive use of the terminology of probability theory. The reader will easily understand however that one speaks in fact about solving enumerative problems of discrete analysis. One of the primary research lines is the study of the limit properties of combinatorial objects manifested at the unlimited increase of the number of elements comprising such objects. It is often possible to represent the distributions of the characteristics of combinatorial objects as conditional distributions of the sums of independent random variables so that they can be studied using asymptotic methods in probability theory, namely limit theorems for sums of independent random variables.},
author = {Pavlov, Yu L.},
doi = {10.1201/9780429469275-8},
file = {:U$\backslash$:/PhD/Papers/Causality/Breiman2001{\_}Article{\_}RandomForests.pdf:pdf},
isbn = {9783110941975},
journal = {Random Forests},
keywords = {classification,ensemble,regression},
pages = {1--122},
title = {{Random forests}},
year = {2019}
}
@article{Chernozhukov2017a,
author = {Chernozhukov, V. and Chetverikov, D. and Demirer, M. and Duflo, E. and Hansen, C. and Newey, W. and Robins, J.},
journal = {The Econometrics Journal},
number = {1},
pages = {C1-C68},
title = {Double/debiased machine learning for treatment and structural parameters},
volume = {21},
year = {2018}
}
@article{Arora1985a,
author = {Arora, J. P.S. and Singh, R. P. and Singhal, V. K. and Singh, S. P.},
file = {:U$\backslash$:/PhD/Papers/Causality/Learning Representations for Counterfactual Inference.pdf:pdf},
issn = {00403490},
journal = {Tenside Detergents},
number = {3},
pages = {123--126},
title = {{Studies on the Binding of Triethanolamine Lauryl Sulphate With Proteins.}},
volume = {22},
year = {1985}
}
@inproceedings{Yoon2018a,
abstract = {Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple treatments) and show that GANITE outperforms state-of-the-art methods.},
author = {Yoon, J. and Jordon, J. and {van der Schaar}, M.},
file = {:U$\backslash$:/PhD/Papers/Causality/ganite{\_}estimation{\_}of{\_}individualized{\_}treatment{\_}effects{\_}using{\_}generative{\_}adversarial{\_}nets.pdf:pdf},
booktitle = {6th International Conference on Learning Representations},
number = {},
pages = {1--22},
title = {{Ganite: Estimation of individualized treatment effects using generative adversarial nets}},
year = {2018}
}
@article{Brodersen2015a,
abstract = {An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response in a synthetic control that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including local trends, seasonality and the time-varying influence of contemporaneous covariates. Using a Markov chain Monte Carlo algorithm for posterior inference, we illustrate the statistical properties of our approach on simulated data. We then demonstrate its practical utility by estimating the causal effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of state-space models in enabling causal attribution in those settings where a randomised experiment is unavailable. The CausalImpact R package provides an implementation of our approach.},
archivePrefix = {arXiv},
arxivId = {arXiv:1506.00356v1},
author = {Brodersen, K. H. and Gallusser, F. and Koehler, J. and Remy, N. and Scott, S. L.},
eprint = {arXiv:1506.00356v1},
file = {:U$\backslash$:/PhD/Papers/Causality/INFERRING CAUSAL IMPACT USING BAYESIAN STRUCTURALTIME-SERIES MODELS.pdf:pdf},
journal = {Annals of Applied Statistics},
keywords = {Advertising,Causal inference,Counterfactual,Difference in differences,Econometrics,Market research,Observational,Synthetic control},
number = {1},
pages = {247--274},
title = {{Inferring causal impact using Bayesian structural time-series models}},
volume = {9},
year = {2015}
}
@article{Shimoni2018,
  title={Benchmarking framework for performance-evaluation of causal inference analysis},
  author={Shimoni, Y. and Yanover, C. and Karavani, E. and Goldschmnidt, Y.},
  journal={arXiv preprint arXiv:1802.05046},
  year={2018}
}
@inproceedings{Ray2018a,
abstract = {We develop a semiparametric Bayesian approach for estimating the mean response in a missing data model with binary outcomes and a nonparametrically modelled propensity score. Equivalently we estimate the causal effect of a treatment, correcting nonparametrically for confounding. We show that standard Gaussian process priors satisfy a semiparametric Bernstein-von Mises theorem under smoothness conditions. We further propose a novel propensity score-dependent prior that provides efficient inference under strictly weaker conditions. We also show that it is theoretically preferable to model the covariate distribution with a Dirichlet process or Bayesian bootstrap, rather than modelling the covariate density using a Gaussian process prior.},
archivePrefix = {arXiv},
arxivId = {1808.04246},
author = {Ray, K. and van der Vaart, A. W.},
eprint = {1808.04246},
file = {:U$\backslash$:/PhD/Papers/Causality/SEMIPARAMETRIC BAYESIAN CAUSAL INFERENCE.pdf:pdf},
issn = {0090-5364},
booktitle = {{Semiparametric Bayesian causal inference}},
url = {http://arxiv.org/abs/1808.04246},
year = {2018}
}
@book{Smith2005,
title={Does matching overcome LaLonde's critique of nonexperimental estimators?},
author={Smith, J. A. and Todd, P. E.},
journal={Journal of econometrics},
volume={125},
number={1-2},
pages={305--353},
year={2005},
publisher={Elsevier}
}
@article{Chipman2012a,
abstract = {We develop a Bayesian "sum-of-trees" model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods in general, and boosting algorithms in particular, BART is defined by a statistical model: a prior and a likelihood. This approach enables full posterior inference including point and interval estimates of the unknown regression function as well as the marginal effects of potential predictors. By keeping track of predictor inclusion frequencies, BART can also be used for model-free variable selection. BART's many features are illustrated with a bake-off against competing methods on 42 different data sets, with a simulation experiment and on a drug discovery classification problem. {\textcopyright} 2010 Institute of Mathematical Statistics.},
author = {Chipman, H. A. and George, E. I. and McCulloch, R. E.},
doi = {10.1214/09-AOAS285},
file = {:U$\backslash$:/PhD/Papers/Causality/BAYESIAN ADDITIVE REGRESSION TREES.pdf:pdf},
issn = {19326157},
journal = {Annals of Applied Statistics},
keywords = {Bayesian backfitting,Boosting,CART,Classification,Ensemble,MCMC,Nonparametric regression,Probit model,Random basis,Regularizatio,Sum-of-trees model,Variable selection,Weak learner},
number = {1},
pages = {266--298},
title = {{BART: Bayesian additive regression trees}},
volume = {6},
year = {2012}
}
@inproceedings{Alaa2017d,
abstract = {Predicated on the increasing abundance of electronic health records, we investigate the problem of inferring individualized treatment effects using observational data. Stemming from the potential outcomes model, we propose a novel multitask learning framework in which factual and counterfactual outcomes are modeled as the outputs of a function in a vector-valued reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian method for learning the treatment effects using a multi-task Gaussian process (GP) with a linear coregionalization kernel as a prior over the vvRKHS. The Bayesian approach allows us to compute individualized measures of confidence in our estimates via pointwise credible intervals, which are crucial for realizing the full potential of precision medicine. The impact of selection bias is alleviated via a risk-based empirical Bayes method for adapting the multi-task GP prior, which jointly minimizes the empirical error in factual outcomes and the uncertainty in (unobserved) counterfactual outcomes. We conduct experiments on observational datasets for an interventional social program applied to premature infants, and a left ventricular assist device applied to cardiac patients wait-listed for a heart transplant. In both experiments, we show that our method significantly outperforms the state-of-the-art.},
archivePrefix = {arXiv},
arxivId = {1704.02801},
author = {Alaa, A. M. and {van der Schaar}, M.},
eprint = {1704.02801},
file = {:U$\backslash$:/PhD/Papers/Causality/6934-bayesian-inference-of-individualized-treatment-effects-using-multi-task-gaussian-processes.pdf:pdf},
issn = {10495258},
booktitle = {Advances in Neural Information Processing Systems},
number = {},
pages = {3424–3432},
title = {{Bayesian inference of individualized treatment effects using multi-task Gaussian processes}},
volume = {},
year = {2017}
}
@article{Heckman1997,
abstract = {This paper considers whether it is possible to devise a nonexperimental procedure for evaluat- ing a prototypical job training programme. Using rich nonexperimental data, we examine the performance of a two-stage evaluation methodology that (a) estimates the probability that a person participates in a programme and (b) uses the estimated probability in extensions of the classical method of matching. We decompose the conventional measure of programme evaluation bias into several components and find that bias due to selection on unobservables, commonly called selection bias in econometrics, is empirically less important than other components, although it is still a sizeable fraction of the estimated programme impact. Matching methods applied to comparison groups located in the same labour markets as participants and administered the same questionnaire eliminate much of the bias as conventionally measured, but the remaining bias is a considerable fraction of experimentally-determined programme impact estimates. We test and reject the identi- fying assumptions that justify the classical method of matching. We present a nonparametric conditional difference-in-differences extension of the method of matching that is consistent with the classical index-sufficient sample selection model and is not rejected by our tests of identifying assumptions. This estimator is effective in eliminating bias, especially when it is due to temporally- invariant omitted variables. 1.},
author = {Heckman, J. J. and Ichimura, H. and Todd, P. E.},
doi = {10.2307/2971733},
file = {:U$\backslash$:/PhD/Papers/Causality/Matching as an Econometric Evaluation Estimatore.pdf:pdf},
issn = {0034-6527},
journal = {The Review of Economic Studies},
number = {4},
pages = {605--654},
title = {{Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme}},
volume = {64},
year = {1997}
}
@book{Mikosch1997,
abstract = {This book explores weak convergence theory and empirical processes and their applications to many applications in statistics. Part one reviews stochastic convergence in its various forms. Part two offers the theory of empirical processes in a form accessible to statisticians and probabilists. Part three covers a range of topics demonstrating the applicability of the theory to key questions such as measures of goodness of fit and the bootstrap.},
author = {Mikosch, T. and der Vaart, A. W. van and Wellner, J. A.},
booktitle = {Journal of the American Statistical Association},
doi = {10.2307/2965734},
file = {:U$\backslash$:/PhD/Papers/Semiparametric Estimation Theory/Weak Convergence and Empirical Processes.pdf:pdf},
isbn = {9781475725476},
issn = {01621459},
number = {438},
pages = {794},
title = {{Weak convergence and empirical processes}},
volume = {92},
year = {1997},
publisher = {Springer Science {\&} Business Media}
}
@book{zalinescu2002convex,
title={Convex analysis in general vector spaces},
author={Zalinescu, C.},
year={2002},
publisher={World scientific}
}
@inproceedings{Louizos2017,
abstract = {Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.},
author = {Louizos, C. and Shalit, U. and Mooij, J. and Sontag, D. and Zemel, R. and Welling, M.},
file = {:U$\backslash$:/PhD/Papers/Causality/Causal Effect Inference with Latent Variables.pdf:pdf},
issn = {10495258},
booktitle = {Advances in Neural Information Processing Systems},
number = {},
pages = {6447--6457},
title = {{Causal effect inference with deep latent-variable models}},
volume = {},
year = {2017}
}
@article{RubinD.B1974,
abstract = {A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating causal effects of treatments. The basic conclusion is that randomization should be employed whenever possible but that the use of carefully controlled nonrandomized data to estimate causal effects is a reasonable and nec-essary procedure in many cases.},
author = {Rubin, D. B.},
file = {:U$\backslash$:/PhD/Papers/Causality/ESTIMATING  CAUSAL  EFFECTS   OF  TREATMENTS  INRANDOMIZED  AND  NONRANDOMIZED  STUDIES.pdf:pdf},
journal = {Journal of Educational Psychology},
number = {5},
pages = {688--701},
title = {{Estimating causal effects of treatment in randomized and nonrandomized studies}},
volume = {66},
year = {1974}
}
@inproceedings{Shalit2017a,
abstract = {There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms leam a "balanced" representation such that the induced treated and control distributions look similar, and we give a novel and intuitive generalization-error bound showing the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.},
archivePrefix = {arXiv},
arxivId = {1606.03976},
author = {Shalit, U. and Johansson, F. D. and Sontag, D.},
eprint = {1606.03976},
file = {:U$\backslash$:/PhD/Papers/Causality/Estimating individual treatment effect generalization bounds and algorithms.pdf:pdf},
isbn = {9781510855144},
booktitle = {34th International Conference on Machine Learning},
pages = {4709--4718},
title = {{Estimating individual treatment effect: Generalization bounds and algorithms}},
volume = {},
year = {2017}
}
@article{Zhang2020a,
abstract = {The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives. Estimating the likely outcome of alternatives from observational data is a challenging problem as all outcomes are never observed, and selection bias precludes the direct comparison of differently intervened groups. Despite their empirical success, we show that algorithms that learn domain-invariant representations of inputs (on which to make predictions) are often inappropriate, and develop generalization bounds that demonstrate the dependence on domain overlap and highlight the need for invertible latent maps. Based on these results, we develop a deep kernel regression algorithm and posterior regularization framework that substantially outperforms the state-of-the-art on a variety of benchmarks data sets.},
archivePrefix = {arXiv},
arxivId = {2001.04754},
author = {Zhang, Yao and Bellot, Alexis and van der Schaar, Mihaela},
eprint = {2001.04754},
file = {:U$\backslash$:/PhD/Papers/Causality/Learning Overlapping Representations for the Estimation ofIndividualized Treatment Effects.pdf:pdf},
title = {{Learning Overlapping Representations for the Estimation of Individualized Treatment Effects}},
url = {http://arxiv.org/abs/2001.04754},
volume = {108},
year = {2020}
}
@article{Wager2018a,
abstract = {Many scientific and engineering challenges—ranging from personalized medicine to customized marketing recommendations—require an understanding of treatment effect heterogeneity. In this article, we develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman's widely used random forest algorithm. In the potential outcomes framework with unconfoundedness, we show that causal forests are pointwise consistent for the true treatment effect and have an asymptotically Gaussian and centered sampling distribution. We also discuss a practical method for constructing asymptotic confidence intervals for the true treatment effect that are centered at the causal forest estimates. Our theoretical results rely on a generic Gaussian theory for a large family of random forest algorithms. To our knowledge, this is the first set of results that allows any type of random forest, including classification and regression forests, to be used for provably valid statistical inference. In experiments, we find causal forests to be substantially more powerful than classical methods based on nearest-neighbor matching, especially in the presence of irrelevant covariates.},
archivePrefix = {arXiv},
arxivId = {1510.04342},
author = {Wager, S. and Athey, S.},
doi = {10.1080/01621459.2017.1319839},
eprint = {1510.04342},
file = {:U$\backslash$:/PhD/Papers/Causality/Estimation and Inference of Heterogeneous Treatment Effects using Random Forests.pdf:pdf},
issn = {1537274X},
journal = {Journal of the American Statistical Association},
keywords = {Adaptive nearest neighbors matching,Asymptotic normality,Potential outcomes,Unconfoundedness},
number = {523},
pages = {1228--1242},
publisher = {Taylor {\&} Francis},
title = {{Estimation and inference of heterogeneous treatment effects using random forests}},
url = {https://doi.org/10.1080/01621459.2017.1319839},
volume = {113},
year = {2018}
}
@article{The2018,
title={The costs of low birth weight},
author={Almond, D. and Chay, K. Y. and Lee, D. S.},
journal={The Quarterly Journal of Economics},
volume={120},
number={3},
pages={1031--1083},
year={2005},
publisher={MIT Press}
}
@article{Parameters2016a,
author = {Parameters, Structural and Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James},
file = {:U$\backslash$:/PhD/Papers/Causality/Double{\_}Debiased Machine Learning for Treatmentand Structural Parameters.pdf:pdf},
title = {{Double / Debiased Machine Learning for Treatment}},
year = {2016}
}
@article{Dehejia2002,
abstract = {This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to the treatment units is difficult because units must be compared across a high-dimensional set of pretreatment characteristics. We discuss the use of propensity score-matching methods, and implement them using data from the National Supported Work experiment. Following LaLonde (1986), we pair the experimental treated units with nonexperimental comparison units from the CPS and PSID, and compare the estimates of the treatment effect obtained using our methods to the benchmark results from the experiment. For both comparison groups, we show that the methods succeed in focusing attention on the small subset of the comparison units comparable to the treated units and, hence, in alleviating the bias due to systematic differences between the treated and comparison units.},
author = {Dehejia, R. H. and Wahba, S.},
doi = {10.1162/003465302317331982},
file = {:U$\backslash$:/PhD/Papers/Causality/Datasets/P R OP   E N  SI  T Y   SC   OR   E -MATC H  I N  G    ME   T H  OD   S   F ORN  ON   E XP   E R I ME   N  T AL     C AU   SAL ST  U  D .pdf:pdf},
issn = {00346535},
journal = {Review of Economics and Statistics},
number = {1},
pages = {151--161},
title = {{Propensity score-matching methods for nonexperimental causal studies}},
volume = {84},
year = {2002}
}
@article{Rubin2005,
abstract = {Causal effects are defined as comparisons of potential outcomes under different treatments on a common set of units. Observed values of the potential outcomes are revealed by the assignment mechanism - a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes. Fisher made tremendous contributions to causal inference through his work on the design of randomized experiments, but the potential outcomes perspective applies to other complex experiments and nonrandomized studies as well. As noted by Kempthorne in his 1976 discussion of Savage's Fisher lecture, Fisher never bridged his work on experimental design and his work on parametric modeling, a bridge that appears nearly automatic with an appropriate view of the potential outcomes framework, where the potential outcomes and covariates are given a Bayesian distribution to complete the model specification. Also, this framework crisply separates scientific inference for causal effects and decisions based on such inference, a distinction evident in Fisher's discussion of tests of significance versus tests in an accept/reject framework. But Fisher never used the potential outcomes framework, originally proposed by Neyman in the context of randomized experiments, and as a result he provided generally flawed advice concerning the use of the analysis of covariance to adjust for posttreatment concomitants in randomized trials. {\textcopyright} 2005 American Statistical Association.},
author = {Rubin, D. B.},
file = {:U$\backslash$:/PhD/Papers/Causality/Causal Inference Using Potential Outcomes.pdf:pdf},
journal = {Journal of the American Statistical Association},
keywords = {Analysis of covariance,Assignment mechanism,Assignment-based causal inference,Bayesian inference,Direct causal effects,Fieller-Creasy,Fisher,Neyman,Observational studies,Principal stratification,Randomized experiments,Rubin causal model},
number = {469},
pages = {322--331},
title = {{Causal inference using potential outcomes: Design, modeling, decisions}},
volume = {100},
year = {2005}
}
@article{Cole2008,
abstract = {The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use. {\textcopyright} The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved.},
author = {Cole, S. R. and Hern{\'{a}}n, M. A.},
doi = {10.1093/aje/kwn164},
file = {:U$\backslash$:/PhD/Papers/Causality/Constructing Inverse Probability Weights for Marginal Structural Models.pdf:pdf},
issn = {00029262},
journal = {American Journal of Epidemiology},
keywords = {Bias (epidemiology),Causality,Confounding factors (epidemiology),Probability weighting,Regression model},
number = {6},
pages = {656--664},
title = {{Constructing inverse probability weights for marginal structural models}},
volume = {168},
year = {2008}
}
@article{Hill2011,
abstract = {Researchers have long struggled to identify causal effects in nonexperimental settings. Many recently proposed strategies assume ignorability of the treatment assignment mechanism and require fitting two models-one for the assignment mechanism and one for the response surface. This article proposes a strategy that instead focuses on very flexibly modeling just the response surface using a Bayesian nonparametric modeling procedure, Bayesian Additive Regression Trees (BART). BART has several advantages: it is far simpler to use than many recent competitors, requires less guesswork in model fitting, handles a large number of predictors, yields coherent uncertainty intervals, and fluidly handles continuous treatment variables and missing data for the outcome variable. BART also naturally identifies heterogeneous treatment effects. BART produces more accurate estimates of average treatment effects compared to propensity score matching, propensity-weighted estimators, and regression adjustment in the nonlinear simulation situations examined. Further, it is highly competitive in linear settings with the "correct" model, linear regression. Supplemental materials including code and data to replicate simulations and examples from the article as well as methods for population inference are available online. Copyright {\textcopyright} 2011 American Statistical Association.},
author = {Hill, J. L.},
doi = {10.1198/jcgs.2010.08162},
file = {:U$\backslash$:/PhD/Papers/Causality/Datasets/Bayesian Nonparametric Modeling for CausalInference.pdf:pdf},
issn = {10618600},
journal = {Journal of Computational and Graphical Statistics},
keywords = {Bayesian,Causal inference,Nonparametrics},
number = {1},
pages = {217--240},
title = {{Bayesian nonparametric modeling for causal inference}},
volume = {20},
year = {2011}
}
@inproceedings{Ray2019a,
title={Debiased Bayesian inference for average treatment effects},
author={Ray, K. and Szabo, B.},
booktitle={Advances in Neural Information Processing Systems},
pages={11929--11939},
year={2019}
}
@article{Farrell2018,
abstract = {We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second-step inference after first-step estimation with deep learning, a result also new to the literature. Our estimation rates and semiparametric inference results handle the current standard architecture: fully connected feedforward neural networks (multi-layer perceptrons), with the now-common rectified linear unit activation function and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed-width, very deep networks. We establish nonasymptotic bounds for these deep nets for a general class of nonparametric regression-type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, such as treatment effects, expected welfare, and decomposition effects. Inference in many other semiparametric contexts can be readily obtained. We demonstrate the effectiveness of deep learning with a Monte Carlo analysis and an empirical application to direct mail marketing.},
archivePrefix = {arXiv},
arxivId = {1809.09953},
author = {Farrell, M. H. and Liang, T. and Misra, S.},
journal = {arXiv preprint arXiv:1809.09953},
eprint = {1809.09953},
file = {:U$\backslash$:/PhD/Papers/Causality/Deep Neural Networks for Estimation and Inference.pdf:pdf},
keywords = {convergence rates,deep learning,neural networks,nonasymptotic bounds,program evaluation,rectified linear unit,semiparametric inference,treatment,treatment effects},
title = {{Deep neural networks for estimation and inference}},
url = {http://arxiv.org/abs/1809.09953},
year = {2018}
}
@article{Benkeser2017,
abstract = {Doubly robust estimators are widely used to draw inference about the average effect of a treatment. Such estimators are consistent for the effect of interest if either one of two nuisance parameters is consistently estimated. However, if flexible, data-adaptive estimators of these nuisance parameters are used, double robustness does not readily extend to inference. We present a general theoretical study of the behaviour of doubly robust estimators of an average treatment effect when one of the nuisance parameters is inconsistently estimated. We contrast different methods for constructing such estimators and investigate the extent to which they may be modified to also allow doubly robust inference. We find that while targeted minimum loss-based estimation can be used to solve this problem very naturally, common alternative frameworks appear to be inappropriate for this purpose.We provide a theoretical study and a numerical evaluation of the alternatives considered. Our simulations highlight the need for and usefulness of these approaches in practice, while our theoretical developments have broad implications for the construction of estimators that permit doubly robust inference in other problems.},
author = {Benkeser, D. and Carone, M. and Laan, M. J. van der and Gilbert, P. B.},
doi = {10.1093/biomet/asx053},
file = {:U$\backslash$:/PhD/Papers/Causality/Average Treatment Effect/Doubly robust nonparametric inference on the averagetreatment effect.pdf:pdf},
issn = {14643510},
journal = {Biometrika},
keywords = {Adaptive estimation,Doubly robust estimation,Efficient influence function,Targeted minimum Loss-Based estimation},
number = {4},
pages = {863--880},
title = {{Doubly robust nonparametric inference on the average treatment effect}},
volume = {104},
year = {2017}
}
@article{Trust2016,
title={The central role of the propensity score in observational studies for causal effects},
author={Rosenbaum, P. R. and Rubin, D. B.},
journal={Biometrika},
volume={70},
number={1},
pages={41--55},
year={1983},
publisher={Oxford University Press}
}
@book{van2011targeted,
author = {van der Laan, M. J. and Rose, S.},
publisher = {Springer Science \& Business Media},
title = {{Targeted learning: causal inference for observational and experimental data}},
year = {2011}
}
@inproceedings{Yao2018a,
abstract = {Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that provides meaningful constraints on the ITE estimation. In this paper, we propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods.},
author = {Yao, L. and Huai, M. and Li, S. and Gao, J. and Li, Y. and Zhang, A.},
file = {:U$\backslash$:/PhD/Papers/Causality/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf:pdf},
issn = {10495258},
booktitle = {Advances in Neural Information Processing Systems},
number = {},
pages = {2633--2643},
title = {{Representation learning for treatment effect estimation from observational data}},
volume = {},
year = {2018}
}
@inproceedings{Shi2019a,
abstract = {This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment (propensity score) for each unit. Second, we plug these fitted models into a downstream estimator of the effect. Neural networks are a natural choice for the models in the first step. The question we address is: how can we adapt the design and training of the neural networks used in the first step in order to improve the quality of the final estimate of the treatment effect? We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects. The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment. The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties `out-of-the-box`. Studies on benchmark datasets for causal inference show these adaptations outperform existing methods. Code is available at github.com/claudiashi57/dragonnet.},
archivePrefix = {arXiv},
arxivId = {1906.02120},
author = {Shi, C. and Blei, D. M. and Veitch, V.},
eprint = {1906.02120},
file = {:U$\backslash$:/PhD/Papers/Causality/Adapting Neural Networks for the Estimation ofTreatment Effects.pdf:pdf},
number = {},
pages = {1--11},
title = {{Adapting neural networks for the estimation of treatment effects}},
booktitle={Advances in Neural Information Processing Systems},
url = {http://arxiv.org/abs/1906.02120},
year = {2019}
}
@misc{Dorie2016,
author = {Dorie, V.},
title = {{NPCI: Non-parametrics for Causal Inference}},
howpublished = {\url{https://github.com/vdorie/npci}},
year = {2016}
}
@inproceedings{alaa2018limits,
  title={Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design},
  author={Alaa, A. M. and van der Schaar, M.},
  booktitle={International Conference on Machine Learning},
  pages={129--138},
  year={2018}
}
@article{ho2007matching,
  title={Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference},
  author={Ho, D. E. and Imai, K. and King, G. and Stuart, E. A.},
  journal={Political analysis},
  volume={15},
  number={3},
  pages={199--236},
  year={2007},
  publisher={Cambridge University Press}
}
@inproceedings{kallus2017recursive,
  title={Recursive partitioning for personalization using observational data},
  author={Kallus, N.},
  booktitle={Proceedings of the 34th International Conference on Machine Learning},
  pages={1789--1798},
  year={2017},
}
@article{alaa2017deep,
  title={Deep counterfactual networks with propensity-dropout},
  author={Alaa, A. M. and Weisz, M. and van der Schaar, M.},
  journal={arXiv preprint arXiv:1706.05966},
  year={2017}
}
@article{schwab2018perfect,
  title={Perfect match: A simple method for learning representations for counterfactual inference with neural networks},
  author={Schwab, P. and Linhardt, L. and Karlen, W.},
  journal={arXiv preprint arXiv:1810.00656},
  year={2018}
}
@article{bang2005doubly,
  title={Doubly robust estimation in missing data and causal inference models},
  author={Bang, H. and Robins, J. M.},
  journal={Biometrics},
  volume={61},
  number={4},
  pages={962--973},
  year={2005},
  publisher={Wiley Online Library}
}
@article{kallus2020generalized,
  title={Generalized optimal matching methods for causal inference},
  author={Kallus, N.},
  journal={Journal of Machine Learning Research},
  volume={21},
  number={62},
  pages={1--54},
  year={2020}
}
@inproceedings{kallus2017framework,
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