Resources

Background reading

Additional resources that you can consult:

Bayesian inference

• Gelman et al., Bayesian Data Analysis, 3rd edition (2013).
• Murphy, Probabilistic Machine Learning: Advanced Topics (2023).

Causal inference

• Imbens and Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (2015).
• Hernán and Robins, Causal Inference: What If (2020).
• Pearl, Causality: Models, Reasoning, and Inference, 2nd edition (2009).

Software

Not really required for the seminar, but might prove to be useful:

• Stan — a probabilistic programming language and platform for Bayesian inference, usable from both R and Python through dedicated interfaces.
• CmdStan — the command-line interface to Stan for compiling models and running inference.
• PyMC and NumPyro — general-purpose probabilistic programming libraries for Bayesian modelling in Python.
• bartCause — an R package for causal-effect estimation using BART.
• dbarts — an R implementation of BART for regression and classification. It is not specifically a causal-inference package.
• stochtree — an R and Python library implementing BART, XBART, Bayesian causal forests (BCF), and related stochastic-tree models.