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Books on the topic 'Dynamic propensity score matching'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Dehejia, Rajeev H. Propensity score matching methods for non-experimental causal studies. National Bureau of Economic Research, 1998.

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2

Caliendo, Marco. Some practical guidance for the implementation of propensity score matching. IZA, 2005.

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3

Essama-Nssah, B. Propensity score matching and policy impact analysis - a demonstration in eviews. World Bank, 2006.

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4

Shapiro, Joseph. Compensatory education for disadvantaged Mexican students: An impact evaluation using propensity score matching. World Bank, 2004.

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5

Heckman, James J. A note on adapting propensity score matching and selection models to choice based samples. National Bureau of Economic Research, 2009.

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6

Heckman, James J. A note on adapting propensity score matching and selection models to choice based samples. National Bureau of Economic Research, 2009.

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7

Kate, Wilkinson, and University of Cape Town. Southern Africa Labour and Development Research Unit, eds. Assessing the incidence of public works programmes: Using propensity score matching techniques to assess the poverty targeting of employment in two public works programmes in South Africa. Southern Africa Labour and Development Research Unit, University of Cape Town, 2009.

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8

Strawiński, Paweł. Propensity Score Matching. Własności. Warsaw University Press, 2014. http://dx.doi.org/10.31338/uw.9788323516965.

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9

Cintina, Inna, and Inessa Love. Re-Evaluating Microfinance: Evidence from Propensity Score Matching. World Bank, Washington, DC, 2017. http://dx.doi.org/10.1596/1813-9450-8028.

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10

Cintina, Inna, and Inessa Love. Re-evaluating Microfinance: Evidence from Propensity Score Matching. Published by Oxford University Press on behalf of the World Bank, 2019. http://dx.doi.org/10.1596/34290.

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11

Vito, Anthony Gennaro. Racial Profiling: Using Propensity Score Matching to Examine Focal Concerns Theory. Taylor & Francis Group, 2016.

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12

Essama-Nssah, B. Propensity Score Matching And Policy Impact Analysis - A Demonstration In Eviews. The World Bank, 2006. http://dx.doi.org/10.1596/1813-9450-3877.

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13

Racial Profiling: Using Propensity Score Matching to Examine Focal Concerns Theory. Taylor & Francis Group, 2017.

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14

Vito, Anthony Gennaro. Racial Profiling: Using Propensity Score Matching to Examine Focal Concerns Theory. Taylor & Francis Group, 2016.

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15

Offender reentry and cognitive intervention: Propensity score matching utility for outcome assessment. LFB Scholarly Publishing LLC, 2014.

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16

Shapiro, Joseph, and Jorge Moreno Trevino. Compensatory Education for Disadvantaged Mexican Students: An Impact Evaluation Using Propensity Score Matching. The World Bank, 2004. http://dx.doi.org/10.1596/1813-9450-3334.

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17

Levin, Ines, and Betsy Sinclair. Causal Inference with Complex Survey Designs. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.4.

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This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of

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18

Gang, Ira N., Rajesh Raj Natarajan, and Kunal Sen. Finance, gender, and entrepreneurship: India’s informal sector firms. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/901-3.

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How does informal economic activity respond to increased financial inclusion? Does it become more entrepreneurial? Does access to new financing options change the gender configuration of informal economic activity and, if so, in what ways and what directions? We take advantage of nationwide data collected in 2010/11 and 2015/16 by India’s National Sample Survey Office on unorganized (informal) enterprises. This period was one of rapid expansion of banking availability aimed particularly at the unbanked, under-banked, and women. We find strong empirical evidence supporting the crucial role of f

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19

Kinuthia, Bethuel Kinyanjui. Agricultural input subsidy and farmers outcomes in Tanzania. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/906-8.

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This paper examines the impact of the government input subsidy—the National Agriculture Input Voucher—on farmers’ production and welfare in Tanzania as well as the factors that influence agricultural production in the country. The analysis is based on the Living Standards Measurement Study-Integrated Surveys on Agriculture for 2008–13. The study uses panel fixed effects and difference-in-difference and propensity score matching methods to examine the two objectives. The results show that the input subsidy programme resulted in an initial increase in maize and rice production but not in the lon

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20

Silva, Napoleão Luiz Costa da, Alice Saccaro, and Jéssica Faciroli. Texto para Discussão 3009. Instituto de Pesquisa Econômica Aplicada (Ipea), 2024. http://dx.doi.org/10.38116/td3009-port.

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O Programa Crescer é um subprograma no âmbito do Programa Nacional de Microcrédito Produtivo Orientado (PNMPO), considerado uma política de microcrédito destinada aos microempreendedores formais e informais de baixa renda. Nesse sentido, fornece crédito sem garantias, com taxas de juros subsidiados pelo governo federal, e vigência de 2011 a 2014. Sob a agenda de concessão de crédito de pequenos valores para empreendedores desassistidos e para pequenas unidades produtivas, este estudo propõe verificar o efeito e a viabilidade econômica do Programa Crescer nas rendas das pessoas beneficiárias, u

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