5 Observational Studies: Sensitivity

Module 5 Slides

Recording: Module 5, Part 1

Recording: Module 5, Part 2.1

Recording: Module 5, Part 2.2

Trivia: Module 5

Textbook reading

Chapters 21 – 22 of Imbens and Rubin (2015)

Chapters 17 – 18 of Ding (2023)

Additional Readings

Cornfield, J., Haenszel, W., Hammond, E.C., Lilienfeld, A.M., Shimkin, M.B., Wynder, E.L. (1959) “Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions,” Journal of the National Cancer Institute, 22(1):173-203.

Schlesselman, J.J. (1978) “Assessing Effects of Confounding Variables,” American Journal of Epidemiology, 108(1):3-8.

Rosenbaum, P.R., Rubin, D.B. (1983) “Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome,” Journal of the Royal Statistical Society: Series B (Methodological), 45(2):212-218.

Manski, C.F. (1990) “Nonparametric Bounds on Treatment Effects,” American Economic Review, 80(2):319-323.

Flanders, W.D., Khoury, M.J. (1990) “Indirect Assessment of Confounding: Graphic Description and Limits on Effect of Adjusting for Covariates,” Epidemiology, 1(3):239-246.

Lin, D.Y., Psaty B.M., Kronmal, R.A. (1998) “Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies,” Biometrics, 54(3):948-963.

Rosenbaum, P.R. (2002) “Observational Studies,” Springer Series in Statistics, Springer-Verlag, New York.

Imbens, G.W. (2003) “Sensitivity to Exogeneity Assumptions in Program Evaluation,” American Economic Review, 93(2):126-132.

MacLehose, R.F., Kaufman, S., Kaufman, J.S., Poole, C. (2005) “Bounding Causal Effects Under Uncontrolled Confounding Using Counterfactuals,” Epidemiology, 16(5):548-555.

Ichino, A., Mealli, F., Nannicini, T. (2008) “From Temporary Help Jobs to Permanent Employment: What Can We Learn from Matching Estimators and their Sensitivity?,” Journal of Applied Econometrics, 23(3):305-327.

Ding, P., VanderWeele, T.J. (2014) “Generalized Cornfield Conditions for the Risk Difference,” Biometrika, 101(4):971-977.

Ding, P., VanderWeele, T.J. (2016) “Sensitivity Analysis Without Assumptions,” Epidemiology, 27(3):368-377.

Dorie, V., Harada, M., Carnegie, N.B., Hill, J. (2016) “A Flexible, Interpretable Framework for Assessing Sensitivity to Unmeasured Confounding,” Statistics in Medicine, 35(20):3453-3470.

VanderWeele, T.J., Ding, P. (2017) “Sensitivity Analysis in Observational Research: Introducing the E-value,” Annals of Internal Medicine, 167(4):268-274.

Franks, A.M., D’Amour, A., Feller, A. (2020) “Flexible Sensitivity Analysis for Observational Studies Without Observable Implications,” Journal of the American Statistical Association, 115(532):1730-1746.

Software

R package causalsens “Methods for Sensitivity Analysis in Causal Inference.”

R package sensatt “A simulation-based sensitivity analysis for matching estimators.”

R package EValue “E-Values for Sensitivity Analyses for Unmeasured Confounding.”