Daniel Malinsky
Daniel Malinsky
Assistant Professor of Biostatistics at Columbia University
Verified email at - Homepage
Cited by
Cited by
Causally interpreting intersectionality theory
LK Bright, D Malinsky, M Thompson
Philosophy of Science 83 (1), 60-81, 2016
Causal discovery algorithms: A practical guide
D Malinsky, D Danks
Philosophy Compass 13 (1), e12470, 2018
Learning Optimal Fair Policies
R Nabi, D Malinsky, I Shpitser
Proceedings of the 36th International Conference on Machine Learning (ICML), 2019
Causal structure learning from multivariate time series in settings with unmeasured confounding
D Malinsky, P Spirtes
Proceedings of 2018 ACM SIGKDD workshop on causal discovery, 23-47, 2018
A Potential Outcomes Calculus for Identifying Conditional Path-Specific Effects
D Malinsky, I Shpitser, T Richardson
Proceedings of the 22nd International Conference on Artificial Intelligence …, 2019
Causal inference under interference and network uncertainty
R Bhattacharya, D Malinsky, I Shpitser
Uncertainty in Artificial Intelligence, 1028-1038, 2020
Differentiable causal discovery under unmeasured confounding
R Bhattacharya, T Nagarajan, D Malinsky, I Shpitser
International Conference on Artificial Intelligence and Statistics, 2314-2322, 2021
Causal Learning for Partially Observed Stochastic Dynamical Systems
SW Mogensen, D Malinsky, NR Hansen
Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence …, 2018
Reconstruction and identification efficiency of inclusive isolated photons
L Carminati, M Delmastro, M Hance, MJ Belenguer, R Ishmukhametov, ...
Technical Report ATL-PHYS-INT-2011-014, CERN, Geneva, 2011
Estimating bounds on causal effects in high-dimensional and possibly confounded systems
D Malinsky, P Spirtes
International Journal of Approximate Reasoning 88, 371-384, 2017
Semiparametric inference for nonmonotone missing-not-at-random data: the no self-censoring model
D Malinsky, I Shpitser, EJ Tchetgen Tchetgen
Journal of the American Statistical Association 117 (539), 1415-1423, 2022
algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD
JD Ramsey, D Malinsky, KV Bui
The Journal of Machine Learning Research 21 (1), 9649-9654, 2020
Estimating causal effects with ancestral graph Markov models
D Malinsky, P Spirtes
Conference on Probabilistic Graphical Models, 299-309, 2016
Intervening on structure
D Malinsky
Synthese 195 (5), 2295-2312, 2018
Learning the structure of a nonstationary vector autoregression
D Malinsky, P Spirtes
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Explaining the behavior of black-box prediction algorithms with causal learning
N Sani, D Malinsky, I Shpitser
arXiv preprint arXiv:2006.02482, 2020
Optimal training of fair predictive models
R Nabi, D Malinsky, I Shpitser
Conference on Causal Learning and Reasoning, 594-617, 2022
Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions
AT Strauss, CN Sidoti, TS Purnell, HC Sung, JW Jackson, S Levin, ...
Liver Transplantation 28 (12), 1841-1856, 2022
Disease-specific contribution of pulvinar dysfunction to impaired emotion recognition in schizophrenia
A Martínez, RH Tobe, PA Gaspar, D Malinsky, EC Dias, P Sehatpour, ...
Frontiers in Behavioral Neuroscience 15, 379, 2022
Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning
JJR Lee, R Srinivasan, CS Ong, D Alejo, S Schena, I Shpitser, ...
The Journal of Thoracic and Cardiovascular Surgery, 2022
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