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[1] T. Shakespeare,et al. Observational Studies , 2003 .
[2] K. Frank,et al. What Would It Take to Change an Inference? Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences , 2013 .
[3] Uri Shalit,et al. Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding , 2021, ICML.
[4] Elias Bareinboim,et al. Sensitivity Analysis of Linear Structural Causal Models , 2019, ICML.
[5] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[6] Charles F. Manski,et al. Confidence Intervals for Partially Identified Parameters , 2003 .
[7] J. Robins,et al. Double/Debiased Machine Learning for Treatment and Structural Parameters , 2017 .
[8] J. Robins,et al. Sensitivity Analyses for Unmeasured Confounding Assuming a Marginal Structural Model for Repeated Measures , 2022 .
[9] Soumendu Sundar Mukherjee,et al. Weak convergence and empirical processes , 2019 .
[10] Victor Chernozhukov,et al. Automatic Debiased Machine Learning of Causal and Structural Effects , 2018 .
[11] G. W. Imbens. Sensitivity to Exogeneity Assumptions in Program Evaluation , 2003 .
[12] M. Blackwell. A Selection Bias Approach to Sensitivity Analysis for Causal Effects , 2014, Political Analysis.
[13] Carlos Cinelli,et al. An Omitted Variable Bias Framework for Sensitivity Analysis of Instrumental Variables , 2022, SSRN Electronic Journal.
[14] Xiaojie Mao,et al. Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding , 2018, AISTATS.
[15] K. Frank. Impact of a Confounding Variable on a Regression Coefficient , 2000 .
[16] Jennifer Hill,et al. Bias Amplification and Bias Unmasking , 2016, Political Analysis.
[17] Edward H. Kennedy,et al. Sensitivity Analysis via the Proportion of Unmeasured Confounding , 2019, 1912.02793.
[18] L. Keele,et al. Identification, Inference and Sensitivity Analysis for Causal Mediation Effects , 2010, 1011.1079.
[19] Ming-Yueh Huang,et al. Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies , 2021, 2104.08300.
[20] Vasilis Syrgkanis,et al. Automatic Debiased Machine Learning via Neural Nets for Generalized Linear Regression , 2021, 2104.14737.
[21] J. Pearl. Causal diagrams for empirical research , 1995 .
[22] James M. Robins,et al. On the Validity of Covariate Adjustment for Estimating Causal Effects , 2010, UAI.
[23] Christopher R. Taber,et al. Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools , 2000, Journal of Political Economy.
[24] James M. Robins,et al. Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers , 2018 .
[25] Victor Chernozhukov,et al. Debiased machine learning of conditional average treatment effects and other causal functions , 2017 .
[26] Jörg Stoye,et al. More on Confidence Intervals for Partially Identified Parameters , 2008 .
[27] Wolfgang Wefelmeyer,et al. A third-order optimum property of the maximum likelihood estimator , 1978 .
[28] Alexander D'Amour,et al. Flexible Sensitivity Analysis for Observational Studies Without Observable Implications , 2018, Journal of the American Statistical Association.
[29] Onyebuchi A Arah,et al. Bias Formulas for Sensitivity Analysis of Unmeasured Confounding for General Outcomes, Treatments, and Confounders , 2011, Epidemiology.
[30] Vasilis Syrgkanis,et al. RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests , 2021, ArXiv.
[31] Paul W. Holland,et al. The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder , 2009, 0905.3463.
[32] James M. Robins,et al. Association, Causation, And Marginal Structural Models , 1999, Synthese.
[33] Masataka Harada,et al. A flexible, interpretable framework for assessing sensitivity to unmeasured confounding , 2016, Statistics in medicine.
[34] J. Angrist,et al. Identification and Estimation of Local Average Treatment Effects , 1995 .
[35] E. C. Hammond,et al. Smoking and lung cancer: recent evidence and a discussion of some questions. , 1959, Journal of the National Cancer Institute.
[36] P. Bickel. Efficient and Adaptive Estimation for Semiparametric Models , 1993 .
[37] Joshua D. Angrist,et al. Mostly Harmless Econometrics: An Empiricist's Companion , 2008 .
[38] Gianluca Detommaso,et al. Causal Bias Quantification for Continuous Treatment , 2021, ArXiv.
[39] Tyler J. VanderWeele,et al. Sensitivity Analysis in Observational Research: Introducing the E-Value , 2017, Annals of Internal Medicine.
[40] Vasilis Syrgkanis,et al. Adversarial Estimation of Riesz Representers , 2020, ArXiv.
[41] J. Robins,et al. Locally Robust Semiparametric Estimation , 2016, Econometrica.
[42] D. Rubin,et al. Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome , 1983 .
[43] Carlos Cinelli,et al. Making sense of sensitivity: extending omitted variable bias , 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).
[44] Karl Pearson,et al. On the General Theory of Skew Correlation and Non-Linear Regression , 2010 .
[45] Nathan Kallus,et al. Confounding-Robust Policy Improvement , 2018, NeurIPS.
[46] E. Oster. Unobservable Selection and Coefficient Stability: Theory and Evidence , 2019 .
[47] J. Pearl. Causal inference in statistics: An overview , 2009 .
[48] M. J. Laan,et al. Targeted Learning: Causal Inference for Observational and Experimental Data , 2011 .
[49] Kjell A. Doksum,et al. Nonparametric Estimation of Global Functionals and a Measure of the Explanatory Power of Covariates in Regression , 1995 .
[50] Jennifer L. Hill,et al. Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder , 2016 .