Wang Miao

1.9k total citations
53 papers, 914 citations indexed

About

Wang Miao is a scholar working on Statistics and Probability, Economics and Econometrics and General Economics, Econometrics and Finance. According to data from OpenAlex, Wang Miao has authored 53 papers receiving a total of 914 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Statistics and Probability, 14 papers in Economics and Econometrics and 6 papers in General Economics, Econometrics and Finance. Recurrent topics in Wang Miao's work include Advanced Causal Inference Techniques (19 papers), Statistical Methods and Bayesian Inference (18 papers) and Statistical Methods and Inference (18 papers). Wang Miao is often cited by papers focused on Advanced Causal Inference Techniques (19 papers), Statistical Methods and Bayesian Inference (18 papers) and Statistical Methods and Inference (18 papers). Wang Miao collaborates with scholars based in United States, China and United Kingdom. Wang Miao's co-authors include M. C. Sunny Wong, Eric J. Tchetgen Tchetgen, Xu Shi, Eric Tchetgen Tchetgen, Zhi Geng, Zhi Geng, Peng Ding, Joseph L. Gastwirth, Jennifer C. Nelson and Zhichao Jiang and has published in prestigious journals such as Journal of the American Statistical Association, Biometrics and International Journal of Molecular Sciences.

In The Last Decade

Wang Miao

45 papers receiving 882 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Wang Miao United States	15	366	214	118	103	87	53	914
Emmanuel Flachaire France	13	216 0.6×	482 2.3×	35 0.3×	159 1.5×	25 0.3×	37	1.0k
Iván Fernández‐Val United States	20	609 1.7×	832 3.9×	115 1.0×	347 3.4×	72 0.8×	48	1.8k
Catalina Bolancé Spain	16	314 0.9×	260 1.2×	92 0.8×	55 0.5×	38 0.4×	67	872
Andreas Alfons Netherlands	12	259 0.7×	81 0.4×	114 1.0×	28 0.3×	18 0.2×	34	636
Marcelo J. Moreira United States	17	556 1.5×	631 2.9×	53 0.4×	539 5.2×	50 0.6×	25	1.6k
David M. Zimmer United States	12	176 0.5×	428 2.0×	83 0.7×	80 0.8×	15 0.2×	58	951
Jennifer Chan Australia	19	318 0.9×	607 2.8×	162 1.4×	45 0.4×	20 0.2×	82	1.6k
Mohamed R. Abonazel Egypt	16	478 1.3×	173 0.8×	31 0.3×	37 0.4×	20 0.2×	90	853
Michael Parzen United States	18	537 1.5×	104 0.5×	126 1.1×	10 0.1×	52 0.6×	36	1.0k
Anabel Forte Spain	13	176 0.5×	164 0.8×	71 0.6×	29 0.3×	28 0.3×	39	777

Countries citing papers authored by Wang Miao

Since Specialization

Citations

This map shows the geographic impact of Wang Miao'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 Wang Miao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wang Miao more than expected).

Fields of papers citing papers by Wang Miao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wang Miao. 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 Wang Miao. The network helps show where Wang Miao may publish in the future.

Co-authorship network of co-authors of Wang Miao

This figure shows the co-authorship network connecting the top 25 collaborators of Wang Miao. A scholar is included among the top collaborators of Wang Miao 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 Wang Miao. Wang Miao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Chen, Yupeng, Yani Wang, Xiaolong Fu, et al.. (2025). The Discovery of Potential Repellent Compounds for Zeugodacus cucuribitae (Coquillett) from Six Non-Favored Hosts. International Journal of Molecular Sciences. 26(14). 6556–6556.

Huang, Haojun, Junhao Zhang, Bang Wang, Wang Miao, & Geyong Min. (2025). Joint Mobile Energy Replenishment and Data Gathering in Wireless Sensor Networks via Federated Deep Reinforcement Learning. IEEE Transactions on Mobile Computing. 24(7). 6460–6473.

Shi, Xu, et al.. (2024). Doubly robust proximal synthetic controls. Biometrics. 80(2). 1 indexed citations

Li, Wei, et al.. (2024). Identification and estimation of causal effects in the presence of confounded principal strata. Statistics in Medicine. 43(22). 4372–4387.

Miao, Wang, et al.. (2024). Existence of robust weak Nash equilibria for leader-follower games with uncertainty. Annals of Operations Research. 356(1). 189–202.

Miao, Wang, et al.. (2023). A Self-Censoring Model for Multivariate Nonignorable Nonmonotone Missing Data. Biometrics. 79(4). 3203–3214. 3 indexed citations

Cui, Yifan, Hongming Pu, Xu Shi, Wang Miao, & Eric Tchetgen Tchetgen. (2023). Semiparametric Proximal Causal Inference. Journal of the American Statistical Association. 119(546). 1348–1359. 19 indexed citations

Li, Wei, et al.. (2023). Proximal causal inference without uniqueness assumptions. Statistics & Probability Letters. 198. 109836–109836. 2 indexed citations

Miao, Wang, et al.. (2023). Data Mining of National Geographical Census for Decision-making in Urban Planning: A Geo-simulation of Urban Size in Beijing, China. Sensors and Materials. 35(3). 965–965.

10.

Li, Hongkai, Wang Miao, Zheng Cai, et al.. (2020). Causal data fusion methods using summary‐level statistics for a continuous outcome. Statistics in Medicine. 39(8). 1054–1067. 3 indexed citations

11.

Shi, Xu, Wang Miao, & Eric Tchetgen Tchetgen. (2020). A Selective Review of Negative Control Methods in Epidemiology. Current Epidemiology Reports. 7(4). 190–202. 88 indexed citations

12.

Miao, Wang, et al.. (2018). Bipartite graphs with every k-matching in a Hamiltonian cycle.. Ars Combinatoria. 136. 181–197. 1 indexed citations

13.

Shi, Xu, Wang Miao, & Eric Tchetgen Tchetgen. (2018). Multiply Robust Causal Inference With Double Negative Control Adjustment for Unmeasured Confounding. arXiv (Cornell University). 1 indexed citations

14.

Liu, Lan, Wang Miao, Baoluo Sun, James M. Robins, & Eric Tchetgen Tchetgen. (2018). Identification and Inference for Marginal Average Treatment Effect on the Treated with an Instrumental Variable. Statistica Sinica. 30(3). 1517–1541. 5 indexed citations

15.

Miao, Wang, Zhi Geng, & Eric J. Tchetgen Tchetgen. (2018). Identifying causal effects with proxy variables of an unmeasured confounder. Biometrika. 105(4). 987–993. 106 indexed citations

16.

Zhao, Yajun, et al.. (2017). Distinct mechanisms in the numerosity processing of random and regular dots. Acta Psychologica. 174. 17–30. 5 indexed citations

17.

Tchetgen, Eric J. Tchetgen, et al.. (2017). Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable. Statistica Sinica. 28(4). 1965–1983. 23 indexed citations

18.

Miao, Wang & Eric J. Tchetgen Tchetgen. (2016). On varieties of doubly robust estimators under missingness not at random with a shadow variable. Biometrika. 103(2). 475–482. 43 indexed citations

19.

Liu, Lan, Wang Miao, Baoluo Sun, James M. Robins, & Eric Tchetgen Tchetgen. (2015). Identification and Inference for Marginal Average Treatment Effect on the Treated With an Instrumental Variable. arXiv (Cornell University). 6 indexed citations

20.

Li, Fan, et al.. (2015). The detection of long-range correlations of operation force and sEMG with multifractal detrended fluctuation analysis. Bio-Medical Materials and Engineering. 26(1_suppl). S1157–68. 2 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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