Office address:  Computer Science Department, Columbia University 
CSB 490  
500 West 120th Street, 10027 New York  
Phone: +1 6469199628. 
Fields of Interest  Curriculum Vitae: CV  

Before joining Columbia I obtained my PhD in Statistics from ETH Zurich and my BMath and MMath from the University of Cambridge. 
Plečko, D. and Bareinboim, E., 2024
Foundations and Trends in Machine Learning, forthcoming.
Fair data adaptation with quantile preservation
Plečko, D. and Meinshausen, N., 2020
Journal of Machine Learning Research, 21(242), pp.144.
fairadapt: Causal Reasoning for Fair Data Preprocessing
Plečko, D., Bennett, N. and Meinshausen, N., 2023
Journal of Statistical Software, forthcoming.
Causal Fairness for Outcome Control
Plečko, D. and Bareinboim, E., 2023
Advances in Neural Information Processing Systems (NeurIPS 2023).
A Causal Framework for Decomposing Spurious Variations
Plečko, D. and Bareinboim, E., 2023
Advances in Neural Information Processing Systems (NeurIPS 2023).
Reconciling Predictive and Statistical Parity: A Causal Approach
Plečko, D. and Bareinboim, E., 2023
arXiv preprint arXiv:2306.05059 (under review in the 38th AAAI Conference on Artificial Intelligence (2024)).
The obesity paradox and hypoglycemia in critically ill patients
Plečko, D., Bennett, N., Mårtensson, J. and Bellomo, R., 2021
Critical Care, 25(1), pp.115.
Plečko, D. et al., 2021
Acta Anaesthesiologica Scandinavica.
ricu: R's Interface to Intensive Care Data
Bennett*, N., Plečko*, D., Ukor, I.F., Meinshausen, N. and Bühlmann, P., 2023
GigaScience, 12, giad041.
Moor*, M., Bennet*, N., Plečko*, D., Horn*, M., Rieck, B., Meinshausen, N., Bühlmann, P. and Borgwardt, K., 2023
Lancet eClinicalMedicine 62.
Rethinking markers of organ failure
Plečko D., Bennett, N., Ukor, I.F. and Bellomo, R., 2020
ISICEM 2020, Abstract P381.
I thoroughly enjoy teaching. In Spring 2023, I offered a 4week course (8 lectures in total) on fair machine learning (within the Causal Inference II course taught by Elias Bareinboim). Below, you can find the course outline and all the necessary materials (including slides, lecture videos, and vignettes for software examples).
Outline:
(L1) Theory of decomposing variations within the total variation fairness measure TV_{x₀, x₁}(y). Explaining the Fundamental Problem of Causal Fairness Analysis. Introducing contrasts and the structural basis expansion for causal fairness measures. Introducing the Explainability Plane. Introducing the simplified cluster causal diagram called the Standard Fairness Model.
(L2) Measures in the TV family. Using contrasts in practice to measure discrimination. Structure of the TV family. Organizing the existing causal fairness measures into the Fairness Map.
(L3) Identification of causal fairness measures from observational data. Estimation of causal fairness measures based on doublyrobust methods and double debiased machine learning.
(L4) Relationship to key existing notions in the fairness literature. Understanding where counterfactual fairness falls in the Fairness Map. Implications of causal fairness for the Fairness Through Awareness framework. Connecting notions of predictive parity and calibration with causal fairness.
Slides: Lectures 3+4
Video: Lectures 3+4
(L5) Introducing the three key tasks of causal fairness analysis: (1) bias detection; (2) fair prediction; (3) fair decisionmaking. Discussing Task 1 of bias detection in depth with applications, including the United States Government Census 2018 dataset, COMPAS dataset & other synthetic examples.
(L6) Discussing Task 2 of fair prediction. Proving the Fair Prediction Theorem that demonstrates why statistical notions of fairness are not sufficient in general.
Slides: Lectures 5, Lectures 6
Vignettes: Census Task 1 Vignette, COMPAS Task 1 Vignette, COMPAS Task 3 Vignette
(L7) Moving beyond the Standard Fairness Model. Discussing how to extend causal fairness analysis to arbitrary causal diagrams. Discussing variablespecific and pathspecific notions of indirect effects. Discussing identifiability and estimation of variablespecific indirect effects.
(L8) Discussing decompositions of spurious effects. Introducing the partial abduction and prediction procedure. Introducing partially abducted submodels. Proving variablespecific spurious decomposition results for Markovian causal models. Proving variablespecific spurious decomposition results for SemiMarkovian causal models.
Slides: Lectures 7+8
Video: Lectures 7+8
I was also involved in teaching during my PhD at ETH Zurich. Below is the list of courses for which I was the course assistant:
I review for the Journal of Machine Learning Research (JMLR), Journal of American Statistical Association (JASA), and for machine learning conferences. I also proudly support the research of the Swiss Sarcoma Network.