Center for Causal Inference (CCI)
Advancing causal inference methods, fostering collaborative research, and providing essential training to drive innovation across diverse fields.
Peer-Reviewed Journal
Observational Studies is a peer-reviewed journal that publishes manuscripts on all aspects of observational studies, including, but not limited to, study protocols for observational studies, methodologies for observational studies, descriptions of data sets for observational studies, software for observational studies, and analyses of observational studies. Researchers from all fields that make use of observational studies are encouraged to submit papers.
Software Resources
CJOINT: AMCE Estimator for Conjoint Experiments
An R implementation of the Average Marginal Component-specific Effects (AMCE) estimator presented in Hainmueller, J., Hopkins, D., and Yamamoto T. (2014) Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments. Political Analysis 22(1):1-30.
Endogenous: Classical Simultaneous Equation Models
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman’s classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <doi:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
FastDR: Fast and Easy Doubly Robust Estimation of Treatments
An R package for fast and easy doubly robust estimation of treatments effects.
IVMODEL: Statistical Inference and Sensitivity Analysis for Instrumental Variables Model
Contains functions for carrying out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics.
IVPACK: Instrumental Variable Estimation
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
MatchMulti: Optimal Multilevel Matching Using a Network Algorithm
Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.
RCBALANCE: Large, Sparse Optimal Matching with Refined Covariate Balance
rcbalance is designed to exploit sparsity among potential treated-control pairings and can conduct matches on a very large scale at low computational cost. Unlike existing packages, it also supports refined covariate balance constraints, which use prioritized lists of nominal co- variates to induce high degrees of balance on the covariates and their interactions, even when it is difficult to find individual pairs that are similar on many covariates. Matching with rcbalance is demonstrated using data from an observational study of right heart catheterization.
Observational Studies paper describing the package
Sensitivity2X2XK: Sensitivity Analysis for 2X2XK Tables in Observational Studies
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
SensitivityCaseControl: Sensitivity Analysis for Case-Control Studies
SensitivityCaseControl R package
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, “Case Definition and Sensitivity Analysis”, Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, “Sensitivity Analysis for Matched Case Control Studies”, Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions.
SensitivityFull: Sensitivity Analysis for Full Matching in Observational Studies
Sensitivity to unmeasured biases in an observational study that is a full match.
Sensitivitymv: Sensitivity Analysis in Observational Studies
Sensitivity analysis in observational studies, including evidence factors and amplification, using the permutation distribution of Huber-Maritz M-statistics, including the permutational t-test.
TWANG: Toolkit for Weighting and Analysis of Nonequivalent Groups
An R package to support causal modeling of observational data through the estimation and evaluation of propensity score weights.
Beth Ann Griffin, Greg Ridgeway, Andrew R. Morral, Lane F. Burgette, Craig Martin, Daniel Almirall, Rajeev Ramchand, Lisa H. Jaycox, Daniel F. McCaffrey. Toolkit for Weighting and Analysis of Nonequivalent Groups (TWANG) Website. Santa Monica, CA: RAND Corporation, 2014. http://www.rand.org/statistics/twang.