4 Observational Studies: Estimation
Module 4 Slides
Recording: Module 4, Part 1.1
Recording: Module 4, Part 1.2
Recording: Module 4, Part 2
Recording Module 4, Part 3.1
Recording Module 4, Part 3.2
Recording Module 4, Part 4
Recording Module 4, Part 5
Trivia 1: Module 4.1
Trivia 2: Module 4.2
Textbook Reading
Chapters 17-20 of Imbens and Rubin (2015)
Additional Readings
Model-Based Imputation
Hill, J.L. (2011) “Bayesian Nonparametric Modeling for Causal Inference,” Journal of Computational and Graphical Statistics, 20(1):217-240.
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., Robins, J. (2018) “Double/debiased machine learning for treatment and structural parameters,” The Econometrics Journal, 21(1):C1-C68.
Blocking/Stratification
Rosenbaum, P.R., Rubin, D.B. (1985) “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score,” The American Statistician, 39(1):33-38.
Lunceford, J.K., Davidian, M. (2004) “Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study,” Statistics in Medicine, 23(19):2937-2960.
Matching Estimators
Abadie, A., Imbens, G.W. (2006) “Large Sample Properties of Matching Estimators for Average Treatment Effects,” Econometrica, 74(1):235-267.
Abadie, A., Imbens, G.W. (2011) “Bias-Corrected Matching Estimators for Average Treatment Effects,” Journal of Business & Economic Statistics, 29(1):1-11.
Stuart, E.A. (2010) “Matching Methods for Causal Inference: A Review and a Look Forward,” Statistical Science, 25(1):1-21.
Weighting Estimators
Hirano, K., Imbens, G.W., Ridder, G. (2003) “Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score,” Econometrica, 71(4):1161-1189.
Robins, J.M., Rotnitzky, A., Zhao, L.P. (1994) “Estimation of regression coefficients when some regressors are not always observed,” Journal of the American Statistical Association, 89(427):846-866.
Li, F., Morgan, K.L., Zaslavsky, A.M. (2018) “Balancing Covariates via Propensity Score Weighting,” Journal of the American Statistical Association, 113(521):390-400.
Li, F., Thomas, L.E., Li, F. (2019) “Addressing Extreme Propensity Scores via the Overlap Weights,” American Journal of Epidemiology, 188(1):250-257.
Hainmueller, J. (2012) “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies,” Political Analysis, 20(1):25-46.
Imai, K., Ratkovic, M. (2014) “Covariate balancing propensity score,” Journal of the Royal Statistical Society: Series B, 76(1):243-263.
Doubly-Robust Approaches
Bang, H., Robins, J.M. (2005) “Doubly Robust Estimation in Missing Data and Causal Inference Models,” Biometrics, 61(4):962-973.
Kang, J.D., Schafer, J.L. (2007) “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data,” Statistical Science, 22(4):523-539.
Kennedy, E.H. (2023) “Toward Optimal Doubly Robust Estimation of Heterogeneous Causal Effects,” Electronic Journal of Statistics, 17 (2) 3008 - 3049.
Robins, J.M., Rotnitzky, A. (1995) “Semiparametric Efficiency in Multivariate Regression Models with Missing Data,” Journal of the American Statistical Association, 90(429):122-129.
Software
MatchIt: R package for implementing matching methods.
WeightIt: R package for generating balancing weights.
CBPS: R package for Covariate Balancing Propensity Score.
optweight: R package for optimal weighting.
CausalGAM: R package for causal inference with splines and the g-computation formula.
BART: R package for Bayesian Additive Regression Trees.