Publications and Preprints

  1. J. Bodik (2024, preprint)
    Extreme Treatment Effect: Extrapolating Causal Effects Into Extreme Treatment Domain

    [code]

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

    [code]

  3. J. Bodik and V. Chavez-Demoulin (2023, preprint)
    Identifiability of causal graphs under nonadditive conditionally parametric causal models

    [code]

  4. J. Bodik, Z. Pawlas, M. Paluš (2023, Extremes)
    Causality in extremes of time series.

    [code]

  5. J. Bodik, L. Mhalla, V. Chavez-Demoulin (2022, preprint)
    Detecting causal covariates for extreme dependence structures

    [code]

  6. 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

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.