The reading group meetings take place in Leiden, on Mondays, 16.00-18.00, and are open to all. If you’d like to be added to the mailing list, please send me an e-mail.
So far, we have read:
- `Causal Inference’ by Miguel Hernán and Jamie Robins, available here.
- `Elements of Causal Inference’ by Peters, Janzing and Schölkopf, available here.
- `Causality’ (2nd edition) by Judea Pearl (chapters 8-10).
Papers to discuss in the future:
- 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.
- 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.