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Randomization of treatment assignment is one of the most powerful tools for the development of modern science. The fields of political science and public policy, which had been long dominated by observational studies, have also begun to witness growing use of experimental studies. Similarly, in public policy, a greater demand for accountability and evidence-based policy-making has led to the use of experiments for program evaluations. Although one may think statistical analysis of randomized experiments is straightforward, this is not usually the case in social science randomized experiments where experimental subjects are human beings. Many complications occur especially when experiments are conducted in the field. In this project, we develop statistical methods for addressing these complications that commonly occur in randomized experiments and program evaluation. In particular, (1) we show that Fisher's randomization inference can be applied to natural experiments when the randomization of treatment assignment may use a complex scheme, (2) we develop statistical methods for dealing with noncompliance, nonresponse, and measurement error problems, (4) we develop statistical methods for identifying and estimating heterogenous treatment effects and interaction effects, and (5) we develop methods for estimating spillover and contagion effects. |
Randomization inference: |
Ho, Daniel E., and Kosuke Imai. (2006). ``Randomization Inference
with Natural Experiments: An Analysis of Ballot Effects in the
2003 California Recall Election.'' Journal of the
American Statistical Association, Vol. 101, No. 475
(September), pp. 888-900. |
Ho, Daniel E., and Kosuke
Imai. (2008). ``Estimating Causal Effects
of Ballot Order from a Randomized Natural Experiment: California
Alphabet Lottery, 1978-2002.'' Public Opinion
Quarterly, Vol. 72, No. 2 (Summer), pp. 216-240. |
Missing data and measurement
error: |
Horiuchi, Yusaku, Kosuke Imai, and
Naoko Taniguchi. (2007). ``Designing and
Analyzing Randomized Experiments: Application to a Japanese
Election Survey Experiment.'' American
Journal of Political Science, Vol. 51, No. 3 (July),
pp. 669-687. |
Imai, Kosuke. (2008).``Sharp Bounds on the Causal Effects in
Randomized Experiments with
``Truncation-by-Death''.'' Statistics & Probability
Letters, Vol. 78, No. 2 (February), pp. 144-149. |
Imai, Kosuke. (2009). ``Statistical Analysis of Randomized
Experiments with Nonignorable Missing Binary Outcomes: An
Application to a Voting Experiment.'' Journal of
the Royal Statistical Society, Series C (Applied
Statistics), Vol. 58, No. 1 (February), pp. 83-104. |
Imai, Kosuke, and Teppei
Yamamoto. (2010). ``Causal
Inference with Differential Measurement Error: Nonparametric
Identification and Sensitivity
Analysis.'' American Journal of Political
Science, Vol. 54, No. 2 (April), pp. 543-560. |
Imai, Kosuke, and Zhichao
Jiang. (2018). ``A
Sensitivity Analysis for Missing Outcomes under the
Matched-Pairs Design.'' Statistics in
Medicine, Vol. 37, No. 20 (September),
pp. 2907-2922. |
Estimation of treatment effect
heterogeneity and interaction effects: |
Imai, Kosuke, and Aaron
Strauss. (2011). ``Estimation of Heterogeneous
Treatment Effects from Randomized Experiments, with Application to
the Optimal Planning of the Get-out-the-vote
Campaign.'' Political Analysis, Vol. 19, No. 1
(Winter), pp. 1-19. (lead article) Winner of Political Analysis
Editors' Choice Award. |
Imai, Kosuke and Marc Ratkovic. (2013).
``Estimating Treatment
Effect Heterogeneity in Randomized Program
Evaluation.'' Annals of Applied Statistics,
Vol. 7, No. 1 (March), pp. 443-470. Winner of the Tom Ten Have
Memorial Award. |
Egami, Naoki, and Kosuke Imai. (2019). ``Causal Interaction in
Factorial Experiments: Application to Conjoint
Analysis.'' Journal of the American Statistical
Association, Vol. 114, No. 526 (June),
pp. 529-540. |
de la Cuesta, Brandon, Naoki Egami, and
Kosuke Imai. ``Improving the External
Validity of Conjoint Analysis: The Essential Role of Profile
Distribution.'' Political Analysis,
Vol. 30, No. 1 (January), pp. 19-45. |
Imai, Kosuke and Michael Lingzhi
Li. (2023). ``Experimental Evaluation
of Individualized Treatment Rules.'' Journal of
the American Statistical Association, Vol. 118, No. 541,
pp. 242-256. |
Goplerud, Max, Kosuke Imai, Nicole E. Pashley. ``Estimating Heterogeneous
Causal Effects of High-Dimensional Treatments: Application to
Conjoint Analysis.'' |
Ham, Dae Woong, Kosuke Imai, and Lucas
Janson. ``Using Machine Learning to
Test Causal Hypotheses in Conjoint Analysis.''
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Estimation of spillover
effects: |
Chattopadhyay, Ambarish, Kosuke Imai, and
Jose R. Zubizarreta. ``Design-based inference for generalized
network experiments with stochastic interventions.''
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Jiang, Zhichao, Kosuke Imai, and Anup
Malani. (2023). ``Statistical Inference and Power
Analysis for Direct and Spillover Effects in Two-Stage Randomized
Experiments.'' Biometrics, Vol. 79, No. 3
(September), pp. 2370-2381. |
Imai, Kosuke, Zhichao Jiang, and Anup
Malani. (2021). ``Causal Inference with
Interference and Noncompliance in Two-Stage Randomized
Experiments.'' Journal of the American
Statistical Association, Vol. 116, No. 534,
pp. 632-644. |
Imai, Kosuke, and Zhichao
Jiang. (2020). ``Identification and
Sensitivity Analysis of Contagion Effects in Randomized
Placebo-Controlled Trials.'' Journal of the Royal
Statistical Society, Series A (Statistics in Society),
Vol. 183, No. 4 (October), pp. 1637-1657. |
Imai, Kosuke. ``experiment: R Package for
Designing and Analyzing Randomized Experiments.''
available through The
Comprehensive R Archive Network. 2007. |
Egami, Naoki, Marc Ratkovic, and Kosuke
Imai. ``FintIt: R Package
for Finding Heterogeneous Treatment Effects.''
available through The
Comprehensive R Archive
Network. 2012-2015. |
National Science Foundation, (2008-2009).
``New Statistical Methods for Randomized Experiments in Political
Science and Public Policy,''
(Political Science Program; SES-0752050). |