Linbo Wang - RESEARCH

I am currently interested in:

See List of publications (chronological order) for a complete list of publications

My research is partially supported by funded projects (as PI or co-PI) from NSERC (multiple grants including a Canada Research Chair and a Discovery Accelerator), CIHR, the Ontario government (Early Researcher Award), the R. Samuel McLaughlin Foundation (Accelerator Grant in Genomic Medicine), the Connaught Foundation (New researcher award), Data Sciences Institute, CANSSI, MITACS (Accelerate) and UTSC (Clusters of Scholarly Prominence Program).

Causal inference with unmeasured confounding

Dingke Tang, Dehan Kong, and Linbo Wang*. The synthetic instrument: From sparse association to sparse causation. [arXiv]

(Winner of ASA Section on Statistics in Epidemiology Early Career Award 2025)

Shi, Xiaochuan, Dehan Kong, and Linbo Wang* (2025+). Simultaneous Estimation of Multiple Treatment Effects from Observational Studies. Journal of Computational and Graphical Statistics, to appear. [arXiv] [journal]
Ying Zhou, Dingke Tang, Dehan Kong, and Linbo Wang* (2024) . Promises of Parallel Outcomes. Biometrika, 111.2 (2024): 537-550. [arXiv] [slides (long)] [slides (short)]
(Winner of IMS Hannan Graduate Student Travel Award 2021)

(Winner of ICSA Student Paper Award 2021)

Dehan Kong, Shu Yang, and Linbo Wang* (2022). Identifiability of causal effects with multiple causes and a binary outcome. Biometrika, 109(1): 265-272. [arXiv] [journal]
Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt (2023). Instrumental variable estimation of the causal hazard ratio (with discussion). Biometrics,79(2), 539-550. [arXiv] [code] [slides]

Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, and Stijn Vansteelandt (2023). Rejoinder. Biometrics,79(2), 564-568. [arXiv]

Fernando Hartwig, Linbo Wang, George Smith, and Neil Davies (2023). Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption. Epidemiology, 34(3), 325-332. [arXiv].
Fernando Hartwig, Linbo Wang, George Smith, and Neil Davies (2022). Homogeneity in the Instrument-exposure Association and Point Estimation Using Binary Instrumental Variables. Epidemiology, 33(6), 828-831. [arXiv] [journal]
Shu Yang, Linbo Wang, and Peng Ding (2019). Causal inference with confounders missing not at random. Biometrika, 106(4): 875-888. [arXiv] [slides]
Linbo Wang and Eric Tchetgen Tchetgen (2018). Bounded, Efficient and Multiply Robust Estimation of Average Treatment Effects Using Instrumental Variables. Journal of the Royal Statistical Society: Series B, 80(3), 531-550. [arXiv] [data & code] [slides]

Variable selection in causal inference

Xinyi Zhang, Linbo Wang, Stanislav Volgushev, Dehan Kong. Fighting Noise with Noise: Causal Inference with Many Candidate Instruments. [arXiv]
Dingke Tang, Dehan Kong, Wenliang Pan, and Linbo Wang* (2023). Ultra-high dimensional variable selection for doubly robust causal inference. Biometrics, 79(2), 903-914. [arXiv] [code] [slides]
Dengdeng Yu, Linbo Wang, Dehan Kong, Hongtu Zhu (2022). Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease. Journal of the American Statistical Association, 117(540), 1656-1668. [arXiv][journal]
Thomas Richardson, James Robins, and Linbo Wang (2018). Discussion of "Data-Driven Confounder Selection via Markov and Bayesian Networks" by Haggstrom J. Biometrics, 74(2), 403-406. [arXiv]
Linbo Wang, James Robins, and Thomas Richardson (2017). On Falsification of the Binary Instrumental Variable Model. Biometrika, 104(1): 229-236. [arXiv] [slides]

Parameterization of discrete graphical models

Linbo Wang, Xiang Meng, Thomas Richardson, and James Robins (2023). Coherent modeling of longitudinal causal effects on binary outcomes. Biometrics, 79(2), 775-787. [arXiv] [journal] [data & code]
Jiaqi Yin, Sonia Markes, Thomas Richardson, and Linbo Wang* (2022). Multiplicative Effect Modeling: The General Case. Biometrika, 109(2), 559-566. [arXiv][journal]
Linbo Wang, Yuexia Zhang, Thomas Richardson, and James Robins (2021). Estimation of local treatment effects under the binary instrumental variable model. Biometrika, 108(4), 881-894. [arXiv] [journal] [code]
Thomas Richardson, James Robins, and Linbo Wang* (2017). On Modeling and Estimation for the Relative Risk and Risk Difference. Journal of the American Statistical Association: Theory and Methods, 519, 1121-1130. [arXiv] [slides (long)] [slides (short)] [poster]

R package brm

Causal inference and optimal transport

Zhenhua Lin, Dehan Kong, and Linbo Wang*. Causal Inference on Non-linear Spaces: Distribution Functions and Beyond. Journal of the Royal Statistical Society: Series B, to appear. [arXiv] [slides] [video]