Publications and Preprints

J. Bodik (2024, preprint)

Extreme Treatment Effect: Extrapolating Causal Effects Into Extreme Treatment DomainJ. Bodik and V. Chavez-Demoulin (2023, preprint)

Structural restrictions in local causal discovery: identifying direct causes of a target variableJ. Bodik and V. Chavez-Demoulin (2023, preprint)

Identifiability of causal graphs under nonadditive conditionally parametric causal models

J. Bodik, Z. Pawlas, M. Paluš (2023, Extremes)

Causality in extremes of time series.

J. Bodik, L. Mhalla, V. Chavez-Demoulin (2022, preprint)

Detecting causal covariates for extreme dependence structures

J. Bodik, Z. Pawlas, M. Paluš (2021, Master thesis, won a 10000KC prize in a competition for best master thesis)

Detection of causality in time series using extreme values.

Selected conferences and talks

CMStatistics (Presented in HTW

**Berlin**, Germany, 15.12.2023-19.12.2023)CUSO PhD day (Presented in UNIGE

**Geneva**, Switzerland, 7.6.2023)EUROCIM: European causal inference meeting (Presented in University of

**Oslo**, Norway, 18.4.2023-22.4.2023)CUSO: statistics and applied probability (Participant in

**Les Diablerets**, Switzerland, 5.2.2023-8.2.2023, 4.9.2022-7.9.2022, 6.2.2022-9.2.2022, 12.9.2021-15.9.2021)BIRS: Combining Causal Inference and Extreme Value Theory in the Study of Climate Extremes and their Causes (Presented via Zoom,

**Kelowna**, Canada, 26.6.2022-1.7.2022)Robust (Presented in

**Prague**, Czech republic, 12.6.2022-17.6.2022)CMStatistics (Presented via Zoom,

**London**, UK, 18.12.2021 - 20.12.2021)Valpred workshop (Presented in

**Aussois**, France, 4.10.2021-7.10.2021)EVA: Extreme value analysys (Presented via Zoom,

**Edinburgh**, UK, 5.6.2021-9.6.2021)

Keywords: Mathematical statistics; Causal inference; Statistical inference; Probability theory; Hypothesis testing; Estimation theory; Confidence intervals; Statistical models; Causality; Counterfactuals; Treatment effects; Observational studies; Randomized experiments; Potential outcomes; Causal diagrams; Identification; Confounding; Selection bias; Propensity scores; Instrumental variables; Mediation analysis; Sensitivity analysis; Bayesian statistics; Nonparametric methods; Structural equation modeling; Granger causality; Directed acyclic graphs (DAGs); Structural causal models; Regression analysis; Time series analysis; Conditional probability; Markov chains; Estimator bias; Maximum likelihood estimation; Resampling methods; Cross-validation; Model selection; Robust statistics; Multivariate analysis; Experimental design; Statistical power; Sequential analysis; Missing data; Latent variable models; Bayesian networks; Marginalization; Causal effect heterogeneity; Principal component analysis; Survival analysis; Time-to-event data; Nonlinear regression; Longitudinal data analysis; Factor analysis; Structural equation models; Bootstrap methods; Spatial statistics; Cluster analysis; Decision trees; Dimensionality reduction; Bayesian hierarchical models; Statistical learning; Machine learning; Classification methods; Regression analysis; Propensity score matching; Network analysis; Granger causality testing; Observational data analysis; Quasi-experimental designs; Robust causal inference; Counterfactual estimation; Time series modeling; Structural equation modeling; Instrumental variable regression; Causal mediation analysis; Nonignorable missing data; Multiple imputation; Factorial designs; Cluster randomized trials; Survival analysis; Propensity-based weighting; Propensity score calibration; Sensitivity analysis; Nonparametric causal inference; Random forests; Causal discovery algorithms; Generalized linear models; Bayesian model averaging; Longitudinal causal inference; Structural causal models; Neural networks; Deep learning; Artificial neural networks; Feedforward networks; Backpropagation; Activation functions; Convolutional neural networks (CNN); Recurrent neural networks (RNN); Long Short-Term Memory (LSTM); Gated Recurrent Units (GRU); Autoencoders; Generative adversarial networks (GAN); Transfer learning; Fine-tuning; Dropout regularization; Batch normalization; Gradient descent; Stochastic gradient descent (SGD); Mini-batch gradient descent; Learning rate; Momentum; Adaptive learning rate methods (e.g., Adam, RMSprop); Hyperparameters; Overfitting; Underfitting; Model capacity; Weight initialization; Convergence criteria; Loss functions; Optimizers.