Hostname: page-component-74656f9884-g96k6 Total loading time: 0 Render date: 2026-11-09T23:55:38.655Z Has data issue: false hasContentIssue false
  • English
  • Français

Introduction to the Virtual Issue: Past and Future Research Agenda on Causal Inference

Published online by Cambridge University Press:  04 January 2017

Kosuke Imai
Kosuke Imai*
Affiliation:
Department of Politics, Princeton University, Princeton NJ 08544, email: kimai@princeton.edu
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
Introduction
Information
Political Analysis , Volume 19 , Virtual Issue V2: Causal Inference and Political Methodology , September 2011 , pp. 1 - 4
Copyright
Copyright © Society for Political Methodology 2011 

References

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.Google Scholar
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.Google Scholar
Bullock, W., Imai, K., and Shapiro, J. N. (2011). Statistical analysis of endorsement experiments: Measuring support for militant groups in Pakistan. Political Analysis Forthcoming.Google Scholar
Caughey, D. and Sekhon, J. (2011). Elections and the regression-discontinuity design: Lessons from close U.S. House races, 1942–2008. Political Analysis Forthcoming.Google Scholar
Corstange, D. (2009). Sensitive questions, truthful answers?: Modeling the list experiment with LISTIT. Political Analysis 17, 1, 45–63.Google Scholar
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.Google Scholar
Dunning, T. (2008). Model specification in instrumental variables regression. Political Analysis 16, 3, 290–302.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
Glynn, A. N. and Quinn, K. M. (2010). An introduction to the augmented inverse propensity weighted estimator. Political Analysis 18, 1, 36–56.CrossRefGoogle Scholar
Glynn, A. N. and Quinn, K. M. (2011). Why process matters for causal inference. Political Analysis 19, 3, 273–286.Google Scholar
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.Google Scholar
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.Google Scholar
Hainmueller, J. (2011). Entropy balancing for causal effects: Multivariate reweighting method to produce balanced samples in observational studies. Political Analysis Forthcoming.Google Scholar
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. Google Scholar
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.Google Scholar
Iacus, S. M., King, G., and Porro, G. (2011). Causal inference without balance checking: Coarsened exact matching. Political Analysis Forthcoming.Google Scholar
Imai, K. (2011). Multivariate regression analysis for the item count technique. Journal of the American Statistical Association 106, 494, 407–416.Google Scholar
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.Google Scholar
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.Google Scholar
King, G. and Zeng, L. (2006). The danger of extreme counterfactuals. Political Analysis 14, 2, 131–159.Google Scholar
Nickerson, D. W. (2005). Scalable protocols offer efficient design for field experiments. Political Analysis 13, 3, 233–252.Google Scholar