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Causal inference with observational studies often suffers from unmeasured confounding, yielding biased estimators based on the unconfoundedness assumption.
Smoking and lung cancer: recent evidence and a discussion of some questions
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Comment: An essay on the logical foundations of survey sampling, part one
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Regression models and life-tables
Cox, D. R. (1972) · 1972
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Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · 1978
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Sensitivity analysis for certain permutation inferences in matched observational studies
Rosenbaum, P. R. (1987) · 1987
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Efficient and Adaptive Estimation for Semiparametric Models
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Robust models in probability sampling (with discussion)
Firth, D. and Bennett, K. E. (1998) · 1998
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On the role of the propensity score in efficient semiparametric estimation of average treatment effects
Hahn, J. (1998) · 1998
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Assessing the sensitivity of regression results to unmeasured confounders in observational studies
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Association, causation, and marginal structural models
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Adjusting for nonignorable drop-out using semiparametric nonresponse models
Scharfstein, D. O., Rotnitzky, A., and Robins, J. M. (1999) · 1999
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The role of the propensity score in estimating dose-response functions
Imbens, G. W. (2000) · 2000
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Relationship between cigarette smoking and novel risk factors for cardiovascular disease in the united states
Bazzano, L. A., He, J., Muntner, P., Vupputuri, S., and Whelton, P. K. (2003) · 2003
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Sensitivity to exogeneity assumptions in program evaluation
Imbens, G. W. (2003) · 2003
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Causal inference with general treatment regimes: Generalizing the propensity score
Imai, K. and Van Dyk, D. A. (2004) · 2004
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Doubly robust estimation in missing data and causal inference models
Bang, H. and Robins, J. M. (2005) · 2005
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Adjusted kaplan–meier estimator and log-rank test with inverse probability of treatment weighting for survival data
Xie, J. and Liu, C. (2005) · 2005
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Large sample properties of matching estimators for average treatment effects
Abadie, A. and Imbens, G. W. (2006) · 2006
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Matched Sampling for Causal Effects
Rubin, D. B. (2006) · 2006
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The hazards of hazard ratios
Hernán, M. A. (2010) · 2010
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Principal stratification analysis using principal scores
Ding, P. and Lu, J. (2017) · 2017
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Assessing the impact of unmeasured confounding for binary outcomes using confounding functions
Kasza, J., Wolfe, R., and Schuster, T. (2017) · 2017
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Sensitivity analysis in observational research: introducing the e-value
VanderWeele, T. J. and Ding, P. (2017) · 2017
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Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., and Robins, J. (2018) · 2018
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Causal inference: a missing data perspective
Ding, P. and Li, F. (2018) · 2018
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Sensitivity analysis for unmeasured confounding in coarse structural nested mean models
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On the origin of risk relativism
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Bias-corrected matching estimators for average treatment effects
Abadie, A. and Imbens, G. W. (2011) · 2011
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Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness, and sensitivity analysis
Tchetgen, E. J. T. and Shpitser, I. (2012) · 2012
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Generalized Cornfield conditions for the risk difference
Ding, P. and VanderWeele, T. J. (2014) · 2014
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Interference and sensitivity analysis
VanderWeele, T. J., Tchetgen, E. J. T., and Halloran, M. E. (2014) · 2014
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Yang, S. and Lok, J. J. (2018) · 2018
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Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap
Zhao, Q., Small, D. S., and Bhattacharya, B. B. (2019) · 2019
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Flexible sensitivity analysis for observational studies without observable implications
Franks, A., D’Amour, A., and Feller, A. (2020) · 2020
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A calibrated sensitivity analysis for matched observational studies with application to the effect of second-hand smoke exposure on blood lead levels in children
Zhang, B. and Small, D. S. (2020) · 2020
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Sharp sensitivity analysis for inverse propensity weighting via quantile balancing
Dorn, J. and Guo, K. (2022) · 2022
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Multiply robust estimation of causal effects under principal ignorability
Jiang, Z., Yang, S., and Ding, P. (2022) · 2022
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Sensitivity analysis for causal effects with generalized linear models
Sjölander, A., Gabriel, E. E., and Ciocănea-Teodorescu, I. (2022) · 2022
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Estimation based on nearest neighbor matching: from density ratio to average treatment effect
Lin, Z., Ding, P., and Han, F. (2023) · 2023
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A First Course in Causal Inference
Ding, P. (2024) · 2024
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