Causality reading group

The focus of this causality reading group is on mathematical theory for causal inference, with some meetings dedicated to methodological developments. The reading group is organized by Aad van der Vaart and me. 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.

If you’re interested in causal inference, the following online seminar may also be of interest to you: here.

The schedule:

Date Location Topic Materials Speaker
December 6, 2021, 16.00-17.30 Online Bonvini, Matteo & Kennedy, Edward. (2020). Sensitivity Analysis via the Proportion of Unmeasured Confounding.    Bart Eggen
November 22, 2021, 16.00-17.30 Online Invariance and causality.   Peter Bühlmann 
October 18, 2021, 16.00-17.30 Online Post, Zhang and Van den Heuvel (2021+). Estimation of individual causal effects in observational studies. slides Richard Post
         
June 14, 2021, 16.00-17.30 Online Ilse, Forré, Welling and Mooij (2021+). Efficient causal inference from combined observational and interventional data through causal reductions.   Joris Mooij
May 10, 2021, 16.00-17.30 Online Optimal adjustment sets.   Andrea Rotnitzky
April 12, 2021, 16.00-17.30 Online Henckel, Perkovic and Maathuis (2019+). Graphical criteria for efficient total effect estimation via adjustment in causal linear models.    Leonard Henckel
March 15, 2021, 16.00-17.30 Online Bucur, Claassen and Heskes (2019). Inferring the direction of a causal link and estimating its effect via a Bayesian Mendelian randomization approach.   Gabriel Bucur
February 22, 2021, 17.00-18.00 Online Lei and Candès (2020+). Conformal inference of counterfactuals and individual treatment effects.   Lihua Lei
2020        
November 23, 2020 Online Bayesian nonparametric modeling, based on:
Hill (2011). Bayesian nonparametric modeling for causal inference.
Hahn, Murray, Carvalho (2020). Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects.
Ray, Van der Vaart (2020+). Semiparametric Bayesian causal inference.
Ray, Szabo (2019). Debiased Bayesian inference for average treatment effects.		   Aad van der Vaart
November 2, 2020 Online Young, Stensrud, Tchetgen Tchetgen, Hernán (2020). A causal framework for classical statistical estimands in failure time settings with competing events. (e-mail Stéphanie or Richard for the slides) Richard Post
October 12, 2020 Online Goetghebeur, Le Cessie, De Stavola, Moodie, Waernbaum (2019+). Formulating causal questions and principled statistical answers.   Saskia le Cessie
September 28, 2020 Online Van Geloven, Swanson, Ramspek, Luijken, Van Diepen, Morris, Groenwold, Van Houwelingen, Putter, Le Cessie (2020). Prediction meets causal inference: the role of treatment in clinical prediction models.   Nan van Geloven
         
March 9, 2020 Snellius, room 176 Robins, Rotnitzky (1995). Semiparametric efficiency in multivariate regression models with missing data.   Lasse Vuursteen
February 24, 2020 LUMC building 2, T-00-022 Diemer, Labrecque, Tiemeier, Swanson (2020). Application of the instrumental inequalities to a Mendelian randomization study with multiple proposed instruments. Background on Mendelian randomization: video & article Sonja Swanson, Lizzie Diemer
February 10, 2020 LUMC building 2, T-00-022 Toh and Hernán (2008). Causal inference from longitudinal studies with baseline randomization.   Bart Eggen
2019        
December 9, 2019 LUMC building 2, T-00-022 Abadie and Imbens (2005). Large sample properties of matching estimators for average treatment effects.   Aad van der Vaart
November 25, 2019 LUMC building 3, V-04-018/022 Joint Causal Inference from Multiple Contexts   Joris Mooij
November 4, 2019 LUMC building 3, V-02-018/022 Hernán (2010). The hazard of hazard ratios.
Aalen, Cook and Røysland (2015). Does Cox analysis of a randomized survival study yield a causal treatment effect? 		 slides Hein Putter
October 14, 2019 LUMC building 3, V-04-052/056 Caliendo and Kopeinig (2005). Some practical guidance for the implementation of propensity score matching. slides 1
slides 2
notes Rubin		 Stéphanie van der Pas
         
May 27, 2019 LUMC building 3, V-03-036 Pearl, Chapters 9 and 10. slides Amine Hadji
May 13, 2019 LUMC building 2, T-00-022 Pearl, Chapter 8.   Richard Gill
April 8, 2019 LUMC building 3, V-02-018/022 Peters et al, Chapter 9. slides Stefan Franssen
March 25, 2019 LUMC building 3, V-02-034 Peters et al, Chapters 4 and 7. slides Richard Post
March 11, 2019 LUMC building 3, V-02-026/028 Peters et al, Chapter 6. slides Richard Gill
February 25, 2019 LUMC building 2, T-00-022 Peters et al, Chapters 1-3. slides Stéphanie van der Pas
2018        
December 17, 2018 LUMC building 3, V-03-044 Hernán & Robins, Chapters 15-16. notes Svetlana Belitser
December 10, 2018 – Extra materials: Note on Inverse Weighing and Double Robustness. note Aad van der Vaart
December 3, 2018 LUMC building 2, T-00-022 Hernán & Robins, Chapters 11-14. slides Richard Post
November 19, 2018 LUMC building 2, T-00-022 Hernán & Robins, Chapters 1-7. notes Aad van der Vaart