The focus of the Leiden causality reading group is on mathematical theory for causal inference, with some meetings dedicated to methodological developments. The reading group meetings take place on Mondays, 16.00-17.30, and are open to all. For the time being, the meetings will take place online. If you’d like to be added to the mailing list, please send me an e-mail.
Another online seminar on causal inference is available here.
The schedule (list of papers to consider for future discussion below):
Papers to discuss in the future:
- Dawid (2020). Decision-theoretic foundations for statistical causality.
- Schölkopf (2019). Causality for machine learning.
- Frangakis, Rubin (2002). Principical stratification in causal inference. and Pearl (2011). Principal stratification – a goal or a tool? and discussion.
- Wang and Blei (2019). The blessings of multiple causes, the discussion by Ogburn, Shpitser and Tchetgen Tchetgen, by D’Amour, and a response by Wang and Blei.
- Maathuis, Kalisch, Bühlmann (2009). Estimating high-dimensional intervention effects from observational data.
- Farrell (2015). Robust inference on average treatment effects with possibly more covariates than observations.
- Colombo, Maathuis, Kalisch, Richardson (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables.
- Maathuis, Colombo (2015). A generalized back-door criterion.
- Robins, Greenland (1992). Identifiability and exchangeability for direct and indirect effects.
- Richardson, Robins (2010). Analysis of the binary instrumental variable model.
- VanderWeele (2019). Principles of confounder selection.
- Shortreed, Ertefaie (2017). Outcome-adaptive lasso: variable selection for causal inference.
- Liu, Kuramoto, Stuart (2014). An introduction to sensitivity analysis for unobserved confounding in non-experimental prevention research.
- Rosenbaum and Rubin (1983). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. (see also Rosenbaum’s Observational Studies, chapter 4)
- Hahn, Todd, Van der Klaauw (2001). Identification and estimation of treatment effects with a regression-discontinuity design. and Imbens, Lemieux (2008). Regression discontinuity designs: a guide to practice.
- Sarvet, Wanis, Stensrud, Hernán (2020). A graphic description of partial exchangeability.