Michela Bia

592 total citations
20 papers, 355 citations indexed

About

Michela Bia is a scholar working on Economics and Econometrics, Statistics and Probability and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Michela Bia has authored 20 papers receiving a total of 355 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Economics and Econometrics, 7 papers in Statistics and Probability and 4 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Michela Bia's work include Advanced Causal Inference Techniques (7 papers), Diabetes Management and Education (4 papers) and Statistical Methods and Inference (4 papers). Michela Bia is often cited by papers focused on Advanced Causal Inference Techniques (7 papers), Diabetes Management and Education (4 papers) and Statistical Methods and Inference (4 papers). Michela Bia collaborates with scholars based in Luxembourg, Italy and United States. Michela Bia's co-authors include Alessandra Mattei, Alfonso Flores‐Lagunes, Carlos A. Flores, Lukáš Lafférs, Martin Huber, Per Kallestrup, Claus Vögele, Charilaos Lygidakis, Jeanine Condo and Brenda Asiimwe‐Kateera and has published in prestigious journals such as Journal of Business and Economic Statistics, BMJ Open and Health and Quality of Life Outcomes.

In The Last Decade

Michela Bia

16 papers receiving 335 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michela Bia Luxembourg 8 140 66 62 43 43 20 355
Joachim Wilde Germany 4 198 1.4× 121 1.8× 27 0.4× 18 0.4× 24 0.6× 13 429
Sean Becketti United States 8 219 1.6× 144 2.2× 27 0.4× 14 0.3× 37 0.9× 23 502
John L. Newman United States 9 123 0.9× 119 1.8× 14 0.2× 16 0.4× 69 1.6× 20 324
Ricardo Mora Spain 10 233 1.7× 147 2.2× 12 0.2× 36 0.8× 24 0.6× 30 458
Amita Majumder India 12 264 1.9× 197 3.0× 10 0.2× 30 0.7× 38 0.9× 46 463
Cristine Campos de Xavier Pinto Brazil 9 194 1.4× 98 1.5× 151 2.4× 5 0.1× 26 0.6× 22 473
Juan Miguel Villa United Kingdom 10 131 0.9× 117 1.8× 8 0.1× 16 0.4× 144 3.3× 27 374
Hilmar Schneider Germany 12 319 2.3× 134 2.0× 69 1.1× 14 0.3× 24 0.6× 61 543
Achille Lemmi Italy 8 172 1.2× 384 5.8× 16 0.3× 18 0.4× 119 2.8× 19 583
Donna Feir Canada 9 88 0.6× 113 1.7× 27 0.4× 7 0.2× 9 0.2× 38 274

Countries citing papers authored by Michela Bia

Since Specialization
Citations

This map shows the geographic impact of Michela Bia's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michela Bia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michela Bia more than expected).

Fields of papers citing papers by Michela Bia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michela Bia. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michela Bia. The network helps show where Michela Bia may publish in the future.

Co-authorship network of co-authors of Michela Bia

This figure shows the co-authorship network connecting the top 25 collaborators of Michela Bia. A scholar is included among the top collaborators of Michela Bia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michela Bia. Michela Bia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bia, Michela, et al.. (2025). What Makes a Satisfying Life? Prediction and Interpretation with Machine‐Learning Algorithms. Review of Income and Wealth. 71(2).
2.
3.
Bia, Michela, Martin Huber, & Lukáš Lafférs. (2023). Double Machine Learning for Sample Selection Models. Journal of Business and Economic Statistics. 42(3). 958–969. 14 indexed citations
4.
Bia, Michela, Alfonso Flores‐Lagunes, & Andrea Mercatanti. (2022). Evaluation of Language Training Programs in Luxembourg using Principal Stratification. 8(1). 1–44. 1 indexed citations
5.
Lygidakis, Charilaos, Michela Bia, Brenda Asiimwe‐Kateera, et al.. (2022). Cultural adaptation and psychometric evaluation of the Kinyarwanda version of the diabetes-39 (D-39) questionnaire. Health and Quality of Life Outcomes. 20(1). 122–122. 3 indexed citations
6.
Lygidakis, Charilaos, et al.. (2021). Quality of life among adult patients living with diabetes in Rwanda: a cross-sectional study in outpatient clinics. BMJ Open. 11(2). e043997–e043997. 8 indexed citations
7.
Lygidakis, Charilaos, Michela Bia, Per Kallestrup, et al.. (2021). Cultural adaptation and psychometric evaluation of the Kinyarwanda version of the problem areas in diabetes (PAID) questionnaire. Health and Quality of Life Outcomes. 19(1). 183–183. 7 indexed citations
8.
Bia, Michela, Alessandra Mattei, & Andrea Mercatanti. (2020). Assessing Causal Effects in a Longitudinal Observational Study With “Truncated” Outcomes Due to Unemployment and Nonignorable Missing Data. Journal of Business and Economic Statistics. 40(2). 718–729. 1 indexed citations
9.
10.
Bia, Michela, Alfonso Flores‐Lagunes, & Andrea Mercatanti. (2019). Evaluation of Language Training Programs in Luxembourg using Principal Stratification. SSRN Electronic Journal.
11.
Bia, Michela, et al.. (2018). The Long-Run Effect of Childhood Poverty and The Mediating Role of Education. Journal of the Royal Statistical Society Series A (Statistics in Society). 182(1). 37–68. 13 indexed citations
12.
Bia, Michela, Alfonso Flores‐Lagunes, & Andrea Mercatanti. (2018). Evaluation of Language Training Programs in Luxembourg Using Principal Stratification. SSRN Electronic Journal.
13.
Bia, Michela, et al.. (2017). The impact of growing up poor in Europe. 449–460. 2 indexed citations
14.
Bia, Michela, Carlos A. Flores, Alfonso Flores‐Lagunes, & Alessandra Mattei. (2014). A Stata Package for the Application of Semiparametric Estimators of Dose–Response Functions. The Stata Journal Promoting communications on statistics and Stata. 14(3). 580–604. 38 indexed citations
15.
Bia, Michela & Philippe Van Kerm. (2014). Space-Filling Location Selection. The Stata Journal Promoting communications on statistics and Stata. 14(3). 605–622. 2 indexed citations
16.
Bia, Michela, Alessandra Mattei, Carlos A. Flores, & Alfonso Flores‐Lagunes. (2013). Semiparametric estimators of dose-response functions. 1 indexed citations
17.
Bia, Michela, et al.. (2013). Measuring intergenerational transmission of poverty. 2 indexed citations
18.
Bia, Michela & Alessandra Mattei. (2012). Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score. Statistical Methods & Applications. 21(4). 485–516. 40 indexed citations
19.
Mattei, Alessandra & Michela Bia. (2009). DOSERESPONSE: Stata module to estimate dose-response function through adjustment for the generalized propensity score. RePEc: Research Papers in Economics. 4 indexed citations
20.
Bia, Michela & Alessandra Mattei. (2008). A Stata Package for the Estimation of the Dose-response Function through Adjustment for the Generalized Propensity Score. The Stata Journal Promoting communications on statistics and Stata. 8(3). 354–373. 209 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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