Abadie, A., Diamond, A., and Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of californias tobacco control program. Journal of the American Statistical Association
105, 490, 493–505.
Bullock, J. G., Green, D. P., and Ha, S. E. (2010). Yes, but what's the mechanism? (don't expect an easy answer). Journal of Personality and Social Psychology
98, 4, 550–558.
Bullock, W., Imai, K., and Shapiro, J. N. (2011). Statistical analysis of endorsement experiments: Measuring support for militant groups in Pakistan. Political Analysis Forthcoming.
Caughey, D. and Sekhon, J. (2011). Elections and the regression-discontinuity design: Lessons from close U.S. House races, 1942–2008. Political Analysis Forthcoming.
Corstange, D. (2009). Sensitive questions, truthful answers?: Modeling the list experiment with LISTIT. Political Analysis
17, 1, 45–63.
Diamond, A. and Sekhon, J. (2011). Genetic matching for estimating causal effects: A new method of achieving balance in observational studies. Working Paper, Department of Political Science, University of California, Berkeley.
Dunning, T. (2008). Model specification in instrumental variables regression. Political Analysis
16, 3, 290–302.
Gerber, A. S., Green, D. P., Kaplan, E. H., and Kern, H. L. (2010). Baseline, placebo, and treatment: Efficient estimation for three-group experiments. Political Analysis
18, 3, 297–315.
Gingerich, D. W. (2010). Understanding off-the-books politics: Conducting inference on the determinants of sensitive behavior with randomized response surveys. Political Analysis
18, 3, 349–380.
Glynn, A. N. and Quinn, K. M. (2010). An introduction to the augmented inverse propensity weighted estimator. Political Analysis
18, 1, 36–56.
Glynn, A. N. and Quinn, K. M. (2011). Why process matters for causal inference. Political Analysis
19, 3, 273–286.
Green, D. P. and Kern, H. L. (2010). Detecting heterogenous treatment effects in large-scale experiments using Bayesian additive regression trees. The Annual Summer Meeting of the Society of Political Methodology, University of Iowa.
Green, D. P., Leong, T. Y., Kern, H. L., Gerber, A. S., and Larimer, C. W. (2009). Testing the accuracy of regression discontinuity analysis using experimental benchmarks. Political Analysis
17, 4, 400–417.
Hainmueller, J. (2011). Entropy balancing for causal effects: Multivariate reweighting method to produce balanced samples in observational studies. Political Analysis Forthcoming.
Hartman, E., Grieve, R., and Sekhon, J. S. (2010). From SATE to PATT: The essential role of placebo test combining experimental and observational studies. The Annual Meeting of the American Political Science Association, Washington D.C.
Ho, D. E., Imai, K., King, G., and Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis
15, 3, 199–236.
Iacus, S. M., King, G., and Porro, G. (2011). Causal inference without balance checking: Coarsened exact matching. Political Analysis Forthcoming.
Imai, K. (2011). Multivariate regression analysis for the item count technique. Journal of the American Statistical Association
106, 494, 407–416.
Imai, K., Keele, L., Tingley, D., and Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review
105, 4, Forthcoming.
Imai, K. and Strauss, A. (2011). Estimation of heterogeneous treatment effects from randomized experiments, with application to the optimal planning of the get-out-the-vote campaign. Political Analysis
19, 1, 1–19.
King, G. and Zeng, L. (2006). The danger of extreme counterfactuals. Political Analysis
14, 2, 131–159.
Nickerson, D. W. (2005). Scalable protocols offer efficient design for field experiments. Political Analysis
13, 3, 233–252.