King, Gary, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T. Moore, Clayton Nall, Nirmala Ravishankar, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas. (2009). ``Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme.'' (with a comment) The Lancet, Vol. 373, No. 9673 (April), pp. 1447-1454.
Background We assessed aspects of Seguro Popular, a programme aimed to deliver health insurance, regular and preventive medical care, medicines, and health facilities to 50 million uninsured Mexicans. Methods We randomly assigned treatment within 74 matched pairs of health clusters – ie, health facility catchment areas – representing 118,569 households in seven Mexican states, and measured outcomes in a 2005 baseline survey (August, 2005, to September, 2005) and follow-up survey 10 months later (July, 2006, to August, 2006) in 50 pairs (n=32 515). The treatment consisted of encouragement to enrol in a health-insurance programme and upgraded medical facilities. Participant states also received funds to improve health facilities and to provide medications for services in treated clusters. We estimated intention to treat and complier average causal effects non-parametrically. Findings Intention-to-treat estimates indicated a 23% reduction from baseline in catastrophic expenditures (1·9% points; 95% CI 0·14-3·66). The effect in poor households was 3·0% points (0·46-5·54) and in experimental compliers was 6·5% points (1·65-11·28), 30% and 59% reductions, respectively. The intention-to-treat effect on health spending in poor households was 426 pesos (39-812), and the complier average causal effect was 915 pesos (147-1684). Contrary to expectations and previous observational research, we found no effects on medication spending, health outcomes, or utilisation. Interpretation Programme resources reached the poor. However, the programme did not show some other effects, possibly due to the short duration of treatment (10 months). Although Seguro Popular seems to be successful at this early stage, further experiments and follow-up studies, with longer assessment periods, are needed to ascertain the long-term effects of the programme. |
Sengupta, Souhardya, Kosuke Imai, and Georgia Papadogeorgou
(2025).
``Low-rank Covariate Balancing Estimators under Interference.''
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Mukaigawara, Mitsuru, and Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou
(2025).
``Spatiotemporal causal inference with arbitrary spillover and carryover effects.''
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Zhou, Lingxiao, and Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou
(2025).
``Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference: How Economic Assistance Moderates the Effects of Airstrikes on Insurgent Violence.''
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Tarr, Alexander and Kosuke Imai
(2025).
``Estimating Average Treatment Effects with Support Vector Machines.''
Statistics in Medicine, Vol. 44, No. 5, e70006.
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Imai, Kosuke, In Song Kim, and Erik Wang
(2023).
``Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.''
American Journal of Political Science, Vol. 67, No. 3 (July), pp. 587-605.
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Fan, Jianqing, Kosuke Imai, Inbeom Lee, Han Liu, Yang Ning, and Xiaolin Yang
(2023).
``Optimal Covariate Balancing Conditions in Propensity Score Estimation.''
Journal of Business & Economic Statistics, Vol. 41, No. 1, pp. 97-110.
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Imai, Kosuke, and Yang Ning
(2023).
``Imai, Kosuke, and Yang Ning. (2023). ``Covariate Balancing Propensity Score.'' Handbook of Matching and Weighting Adjust.''
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Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li
(2023).
``Distributionally Robust Causal Inference with Observational Data.''
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Papadogeorgou, Georgia, Kosuke Imai, Jason Lyall, and Fan Li
(2022).
``Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq.''
Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 84, No. 5 (November), pp. 1969-1999.
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Imai, Kosuke and In Song Kim
(2021).
``On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data.''
Political Analysis, Vol. 29, No. 3 (July), pp. 405-415.
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Imai, Kosuke and James Lo
(2021).
``Robustness of Empirical Evidence for the Democratic Peace: A Nonparametric Sensitivity Analysis.''
International Organization, Vol. 75, No. 3 (Summer), pp. 901-919.
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Ning, Yang, Sida Peng, and Kosuke Imai
(2020).
``Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score.''
Biometrika, Vol. 107, No. 3 (September), pp. 533-554.
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Zhao, Shandong, David A. van Dyk, and Kosuke Imai
(2020).
``Propensity-Score Based Methods for Causal Inference in Observational Studies with Non-Binary Treatments.''
Statistical Methods in Medical Research, Vol. 29, No. 3 (March), pp. 709-727.
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Imai, Kosuke and In Song Kim
(2019).
``When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?.''
American Journal of Political Science, Vol. 63, No. 2 (April), pp. 467-490.
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Imai, Kosuke, and Zhichao Jiang
(2018).
``A Sensitivity Analysis for Missing Outcomes Due to Truncation-by-Death under the Matched-Pairs Design.''
Statistics in Medicine, Vol. 37, No. 20 (September), pp. 2907-2922.
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Fong, Christian, Chad Hazlett, and Kosuke Imai
(2018).
``Covariate Balancing Propensity Score for a Continuous Treatment: Application to the Efficacy of Political Advertisements.''
Annals of Applied Statistics, Vol. 12, No. 1, pp. 156-177.
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Imai, Kosuke and Marc Ratkovic
(2015).
``Robust Estimation of Inverse Probability Weights for Marginal Structural Models.''
Journal of the American Statistical Association, Vol. 110, No. 511 (September), pp. 1013-1023.
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Imai, Kosuke and Marc Ratkovic
(2014).
``Covariate Balancing Propensity Score.''
Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 76, No. 1 (January), pp. 243-263.
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Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth Stuart
(2011).
``MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.''
Journal of Statistical Software, Vol. 42, No. 8 (Special Volume on Political Methodology), pp. 1-28.
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Imai, Kosuke, Gary King, and Clayton Nall
(2009).
``Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments.''
Statistical Science, Vol. 24, No. 1 (February), pp. 65-72.
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Imai, Kosuke, Gary King, and Clayton Nall
(2009).
``The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation.''
(with discussions and rejoinder) Statistical Science, Vol. 24, No. 1 (February), pp. 29-53.
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Imai, Kosuke
(2008).
``Variance Identification and Efficiency Analysis in Randomized Experiments under the Matched-Pair Design.''
Statistics in Medicine, Vol. 27, No. 24 (October), pp. 4857-4873.
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Imai, Kosuke, Gary King, and Elizabeth A. Stuart
(2008).
``Misunderstandings among Experimentalists and Observationalists about Causal Inference.''
Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171, No. 2 (April), pp. 481-502. Reprinted in Field Experiments and their Critics, D. Teele ed. (2014), New Haven: Yale University Press.
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Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart
(2007).
``Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.''
Political Analysis, Vol. 15, No.3 (Summer), pp. 199-236. Winner of Miller Prize.
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Imai, Kosuke
(2005).
``Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments.''
American Political Science Review, Vol. 99, No. 2 (May), pp. 283-300.
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Imai, Kosuke, and David A. van Dyk
(2004).
``Causal Inference With General Treatment Regimes: Generalizing the Propensity Score.''
Journal of the American Statistical Association, Vol. 99, No. 467 (September), pp. 854-866.
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