Fetching the paper…
Reading the bibliography…
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data.
On the application of probability theory to agricultural experiments. essay on principles. section 9
Neyman, J. (1990 [1923]) · 1923
Earlier work this paper cites.
Smoking and lung cancer: recent evidence and a discussion of some questions
Cornfield, J., W. Haenszel, E. C. Hammond, A. M. Lilienfeld, M. B. Shimkin, and E. L. Wynder (1959) · 1959
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
Earlier work this paper cites.
Sample selection bias as a specification error
Heckman, J. J. (1979) · 1979
Earlier work this paper cites.
A comparison of alternative models for the demand for medical care
Duan, N., W. G. Manning, C. N. Morris, and J. P. Newhouse (1983) · 1983
Earlier work this paper cites.
Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome
Rosenbaum, P. R. and D. B. Rubin (1983) · 1983
Earlier work this paper cites.
Mixture Distributions—I
Everitt, B. S. (1985) · 1985
Earlier work this paper cites.
Discussion 4: Mixture modeling versus selection modeling with nonignorable nonresponse
Holland, P. (1986) · 1986
Earlier work this paper cites.
National Health and Nutrition Examination Survey Data III, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Hyattsville, MD
Centers for Disease Control and Prevention (CDC) (1997) · 1997
Earlier work this paper cites.
Making the most out of programme evaluations and social experiments: Accounting for heterogeneity in programme impacts
Heckman, J. J., J. Smith, and N. Clements (1997) · 1997
Earlier work this paper cites.
Selection models for repeated measurements with non-random dropout: an illustration of sensitivity
Kenward, M. G. (1998) · 1998
Earlier work this paper cites.
Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
Scharfstein, D. O., A. Rotnitzky, and J. M. Robins (1999, December) · 1999
Earlier work this paper cites.
Reparameterizing the pattern mixture model for sensitivity analyses under informative dropout
Daniels, M. J. and J. W. Hogan (2000) · 2000
Earlier work this paper cites.
Markov chain sampling methods for dirichlet process mixture models
Neal, R. M. (2000) · 2000
Earlier work this paper cites.
Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models
Robins, J. M., A. Rotnitzky, and D. O. Scharfstein (2000) · 2000
Earlier work this paper cites.
A two-part random-effects model for semicontinuous longitudinal data
Olsen, M. K. and J. L. Schafer (2001) · 2001
Earlier work this paper cites.
Methods for conducting sensitivity analysis of trials with potentially nonignorable competing causes of censoring
Rotnitzky, A., D. Scharfstein, T.-L. Su, and J. Robins (2001) · 2001
Earlier work this paper cites.
Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings
Abadie, A., J. Angrist, and G. Imbens (2002) · 2002
Earlier work this paper cites.
Pattern-mixture and selection models for analysing longitudinal data with monotone missing patterns
Birmingham, J., A. Rotnitzky, and G. M. Fitzmaurice (2003) · 2003
Earlier work this paper cites.
Sensitivity to exogeneity assumptions in program evaluation
Imbens, G. W. (2003) · 2003
Earlier work this paper cites.
Multiple imputation for incomplete data with semicontinuous variables
Javaras, K. N. and D. A. Van Dyk (2003) · 2003
Cited alongside, same era.
Basic concepts of statistical inference for causal effects in experiments and observational studies
Rubin, D. B. (2003) · 2003
Cited alongside, same era.
Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes
Scharfstein, D. O., M. J. Daniels, and J. M. Robins (2003) · 2003
Cited alongside, same era.
Causal inference with general treatment regimes: Generalizing the propensity score
Imai, K. and D. A. Van Dyk (2004) · 2004
Cited alongside, same era.
What mean impacts miss: Distributional effects of welfare reform experiments
Bitler, M. P., J. B. Gelbach, and H. W. Hoynes (2006) · 2006
Cited alongside, same era.
Amplification of sensitivity analysis in matched observational studies
A bayesian partial identification approach to inferring the prevalence of accounting misconduct
Hahn, P. R., J. S. Murray, and I. Manolopoulou (2016) · 2016
Later among the works it cites.
Bias amplification and bias unmasking
Middleton, J. A., M. A. Scott, R. Diakow, and J. L. Hill (2016) · 2016
Later among the works it cites.
A propensity-score-adjustment method for nonignorable nonresponse
Riddles, M. K., J. K. Kim, and J. Im (2016) · 2016
Later among the works it cites.
Athey, S. and S. Wager (2017) · 2017
Later among the works it cites.
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects
Hahn, P. R., J. S. Murray, and C. M. Carvalho (2017) · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rosenbaum, P. R. and J. H. Silber (2009) · 2009
Cited alongside, same era.
Bart: Bayesian additive regression trees
Chipman, H. A., E. I. George, R. E. McCulloch, et al. (2010) · 2010
Cited alongside, same era.
Targeted learning: causal inference for observational and experimental data
Van der Laan, M. J. and S. Rose (2011) · 2011
Cited alongside, same era.
Bayesian nonparametric modeling for causal inference
Hill, J. L. (2012) · 2012
Cited alongside, same era.
Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems
Díaz, I. and M. J. van der Laan (2013) · 2013
Cited alongside, same era.
Bayesian data analysis
Gelman, A., H. S. Stern, J. B. Carlin, D. B. Dunson, A. Vehtari, and D. B. Rubin (2013) · 2013
Cited alongside, same era.
A selection bias approach to sensitivity analysis for causal effects
Blackwell, M. (2014) · 2014
Cited alongside, same era.
Bayesian nonparametric analysis of longitudinal studies in the presence of informative missingness
Linero, A. R. (2017) · 2017
Later among the works it cites.
Bayesian approaches for missing not at random outcome data: The role of identifying restrictions
Linero, A. R. and M. J. Daniels (2017) · 2017
Later among the works it cites.
Unobservable selection and coefficient stability: Theory and evidence
Oster, E. (2017) · 2017
Later among the works it cites.
Observation and Experiment: An Introduction to Causal Inference
Rosenbaum, P. (2017) · 2017
Later among the works it cites.
Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap
Zhao, Q., D. S. Small, and B. B. Bhattacharya (2017) · 2017
Later among the works it cites.
Making sense of sensitivity: Extending omitted variable bias
Cinelli, C. and C. Hazlett (2018) · 2018
Closest in time.
Decomposing treatment effect variation
Ding, P., A. Feller, and L. Miratrix (2018) · 2018
Closest in time.
When is a sensitivity parameter exactly that?
Gustafson, P., L. C. McCandless, et al. (2018) · 2018
Closest in time.
Algorithmic decision making in the presence of unmeasured confounding
Jung, J., R. Shroff, A. Feller, and S. Goel (2018) · 2018
Closest in time.
Estimating bayesian optimal treatment regimes for dichotomous outcomes using observational data
Klausch, T., P. van de Ven, T. van de Brug, R. H. Brakenhoff, M. A. van de Wiel, and J. Berkhof (2018) · 2018
Closest in time.
dirichletprocess: Build Dirichlet Process Objects for Bayesian Modelling
Ross, G. J. and D. Markwick (2018) · 2018
Closest in time.
The blessings of multiple causes
Wang, Y. and D. M. Blei (2018) · 2018
Closest in time.
A bayesian nonparametric approach to causal inference on quantiles
Xu, D., M. J. Daniels, and A. G. Winterstein (2018) · 2018
Closest in time.
On multi-cause causal inference: Impossibility, sensitivity, and the promise of proxies
D’Amour, A. (2019) · 2019
Closest in time.